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MODELING ROAD AND RAIL FREIGHT
ENERGY CONSUMPTION:
A COMPARATIVE STUDY
ASHIS PARAJULI
2005
________________________________________________________________________
MODELLING ROAD AND RAIL FREIGHT ENERGY CONSUMPTION:
A COMPARATIVE STUDY
ASHIS PARAJULI
BEng (CIVIL)
SCHOOL OF URBAN DEVELOPMENT
QUEENSLAND UNIVERSITY OF TECHNOLOGY
2005
SUBMISSION FOR THE DEGREE OF
MASTER OF ENGINEERING
________________________________________________________________________
Keywords Freight task, Road freight energy consumption, Rail freight energy consumption, Pick-up leg, Line-haul, Delivery leg, Payload.
ABSTRACT
After reviewing land based freight growth trends nationally and internationally, this
thesis discusses the main parameters governing fuel consumption, as well as past
approaches in modelling road and rail energy consumption. Past work on comparing
these two main modes is also reviewed here. The review included ways of estimating
energy consumption of a complete freight task i.e., from origin to destination.
Mathematical models estimating modal energy consumption are presented in this thesis.
Modal energy consumption is a complex function to be approximated in practice due to
numerous variables affecting their outcome. Energy demands are particularly sensitive
to changes in vehicle characteristics such as mass and size; route parameters such as
grade and curvature; traffic conditions such as level of congestion; and less sensitive to
ambient conditions, such as temperature and altitude.
There is a large set of energy estimation models available to transportation planners.
Unfortunately, unless simple relationships are established for energy estimation and
modal comparison, their application in freight movement planning and corridor
development becomes computationally prohibitive.
This thesis describes the development of a modal freight energy comparison tool to
quantify the energy advantage from mode choice, corridor development and vehicle
types and loading improvements. The thesis also describes the used modelling processes
and the trade-offs between model complexity and data quality.
The tool developed in this thesis is based on well established relationships between
energy consumption and traffic flow, route and vehicle operating characteristics for road
freight movement. The rail freight component was developed from equations of motion
together with parameters obtained from past studies. The relationships have been
enhanced to fit the purpose of corridor level comparative analysis. The comparison tool
has been implemented using a spreadsheet based approach developed specifically to
calculate the total door to door energy consumption for given task options. A series of
linked sheets enable the user to: specify all necessary inputs; estimate road and rail
energy by trip segment. The outputs consist of trip segment energy demand and total
energy efficiency of each option.
A case study approach, for aiding in model development and testing, is presented.
Toowoomba second range crossing in Southern Queensland, Australia (section between
below Postman’s Ridge and Gowrie Junction) was selected. Four options considered
include existing and proposed road and rail corridors. The existing rail and road
corridors could be taken as a typical poor case, with very high grades and sharp
curvatures. The proposed new road section has a relaxed curvature and gradient. The
section of proposed rail corridor, under consideration here, still contains a high grade
section. However, the proposed track length is considerably shorter than the base-case.
The new proposed train alignment was found as the most efficient mode and the existing
trains as the least efficient mode when measured based on absolute expected fuel gain
(litres/tonnage of freight moved). This could be attributed to the improvement in
curvature and load carrying capacity. However, when the options are compared in terms
of litres/1000 NTK, the new train option did not show a significant advantage.
Furthermore, the developed model was applied on some simulated cases to test the
functionality of other aspects of the model. The total door-to-door energy consumption
and the efficiency were compared for all the simulated cases. It showed that the energy
efficiency of scenarios varies exponentially with the variation in the ratio of road pick-
up and delivery legs to the rail line-haul length. In general, energy efficiency of the
intermodal options was found to be better unless the best case of the road and the worst
case of intermodal option was compared.
The modelling approaches presented in the thesis and the comparison model developed
in this study could be used for several purposes namely: to assess the energy (and hence
greenhouse gas) implications of specific modal freight movements; to aid in the
economic and environmental evaluation of transport options; and to assess the potential
for energy efficiency gains from vehicle and infrastructure improvements. A number of
suggested improvements to the model are also discussed.
Table of Contents Chapter One Introduction
Chapter Two Freight Trends: Task and Energy 2.1 Introduction 5 2.2 Freight Growth 6 2.2.1 International 7 2.2.2 Domestic 8 2.3 Rail and road freight movements 8 2.3.1 Bulk freight movements 8 2.3.2 Non-bulk freight movements 8 2.3.3 Competitive neutrality between road and rail 9 2.4 Main mode characteristics 10 2.4.1 Road Freight vehicles and units 10 2.4.2 Rail Freight locomotives and units 12 2.5 Energy in freight: Trends 14 2.5.1 Energy consumption trends 14 2.5.2 Energy efficiency trends 15 Chapter Three Estimating Modal Energy Consumption
1.1 Background 1 1.2 Scope 2 1.3 Structure of the thesis 2
3.1 Introduction 19 3.2 Factors affecting energy consumption 19 3.2.1 Fuel and energy content 20 3.2.2 Road transport 23 3.2.3 Rail transport 25 3.3 Energy consumption models 27 3.3.1 Road transport 27 3.3.2 Rail transport 33 3.4 Energy consumption: Comparative studies 35 3.4.1 Introduction to Intermodal transport 36 3.4.2 Previous comparative methodologies 37 3.4.3 Factors influencing comparative studies 40 3.4.4 Limitations of comparative studies 41 3.5 Conclusions and implications 42 3.5.1 Conclusions from the literature review 42 3.5.2 Implications for the thesis 43
Chapter Four Model Development
Chapter Five Sensitivity Analysis
4.1 Introduction 45 4.2 General Model Requirements 46 4.2.1 Selecting the fuel efficiency measuring unit 46 4.2.2 Classifying the commodities 46 4.2.3 Route characteristics 46 4.2.4 Determining vehicle characteristics 47 4.2.5 Data collection 47 4.3 Road transport sub-model 48 4.3.1 Background 48 4.3.2 Amendments to NIMPAC algorithm 51 4.3.3 Adjustment factors 52 4.3.4 Summary for road 58 4.3.5 Vehicle simulator 58 4.4 Rail transport sub-model 60 4.5 Additional transport process sub-model 65 4.5.1 Intermodal transfer energy 65 4.5.2 Shunting energy 66 4.6 Spreadsheet model platform 66 4.7 Summary 70
5.1 Introduction 71 5.2 Model Errors 71 5.3 Errors and uncertainty in road energy estimation 73 5.3.1 Background 73 5.3.2 Roughness sensitivity 74 5.3.3 Speed coefficients and speed sensitivity 74 5.3.4 Grade sensitivity 76 5.3.5 Curvature sensitivity 78 5.3.6 Congestion sensitivity 80 5.3.7 Payload sensitivity 81 5.3.8 Sensitivity summary of road sub-model 81 5.4 Errors and uncertainty in rail energy estimation 82 5.4.1 Background 82 5.4.2 Train length 83 5.4.3 Train Mass 84 5.4.4 Train Speed 85 5.4.5 Grade and curvature 85 5.4.6 Numbers of Wagons and Locomotives 87 5.4.7 Sensitivity summary of rail sub-model 87 5.5 Model complexity and input data 89
Chapter Six Case Study
Chapter Seven Model Application: Simulated Cases 7.1 Background 116 7.2 Route specification and comparison scenarios 117 7.3 Energy estimation 120 7.3.1 Scenario One to Six 120 7.3.2 Scenario Seven to Twelve 122 7.3.3 Scenario Thirteen to Eighteen 125 7.3.4 Scenario Nineteen to Twenty Four 127 7.3.5 Scenario Twenty Five to Twenty Eight 128 7.4 Overall results 129 Chapter Eight Conclusions and Future Research 8.1 Literature Review 133 8.2 Model Development and sensitivity of model parameters 133 8.3 Case Study 134 8.4 Model application: On Simulated Cases 134 8.5 Future Research 136 References Appendices
6.1 Introduction 90 6.2 Site description 90 6.2.1 Background 90 6.2.2. Option One (Existing Road) 92 6.2.3 Option Two (Existing Rail) 98 6.2.4 Option Three (Proposed Road Alignment) 100 6.2.5 Option Four (Proposed Rail) 103 6.3 Freight description 104 6.4 Energy estimation 105 6.4.1 Option One (Existing Road) 105 6.4.2 Option Two (Existing Rail) 109 6.4.3 Option Three (Proposed Road Alignment) 111 6.4.4 Option Four (Proposed Rail) 113 6.4.5 Options comparison 114
List of Figures 1.1 Structure of the thesis 3 2.1 Structure of Chapter 5 2.2 The interstate non-bulk freight task: Trends 8 2.3 Types of combination vehicles 11 2.4 Transport Energy Consumption in EU (15 countries) 14 2.5 Freight Energy Consumption in Australia 15 2.6 Australian domestic freight energy efficiency 17 3.1 Structure of Chapter II 19 3.2 Energy Train for a typical urban car 24 3.3 Model of energy flow in vehicle 25 3.4 ARFCOM approach to modeling fuel consumption 31 3.5 Intermodal transfer of various carriage units 37 3.6 Factors influencing a comparative study 40 3.7 Comparison routes 44 4.1 Overview of model development methodology 45 4.2 Fuel consumption versus vehicle speed 50 4.3 Relationship between load and fuel consumption correction factor 51 4.4 Fuel consumption versus grade 53 4.5 Fuel consumption versus congestion 55 4.6 Fuel consumption versus road roughness 57 4.7 Effect of Gross Vehicle Mass in Energy consumption 58 4.8 Relationships between payload and energy consumption 59 4.9 Gauge width dimension 62 4.10 Flow diagram of the comparison spreadsheet tool 67 4.11 Input rail sheet 68 4.12 Output road sheet 69 4.13 Summary sheet 70 5.1 Error versus complexity 72 5.2 Roughness sensitivity 74 5.3a Speed sensitivity (constant coefficient variation, A) 75 5.3b Speed sensitivity (reciprocal coefficient variation, B) 75 5.3c Speed sensitivity (square coefficient variation, C) 76 5.4a Grade sensitivity at 2% gradient 77 5.4b Grade sensitivity at 4% gradient 77 5.4c Grade sensitivity at 8% gradient 77 5.5a Curvature sensitivity for very curvy section 78 5.5b Curvature sensitivity for less curvy section 78 5.6a Congestion sensitivity at light traffic section 80 5.6b Congestion sensitivity at heavy traffic section 80 5.7 Effect of variation in train length 83
5.8 Effect of variation in train mass 84 5.9 Effect of variation in train speed 85 5.10 Effect of variation in route gradient 86 5.11 Effect of variation in curvature radius 86 5.12 Effect of variation of Number of Axles 87 5.13 Sensitivity Comparison of various parameters 88 6.1 Road options 92 6.2 Rail options 92 6.3 Grade profile (Postman Ridge to entrance of Toowoomba city) 95 6.4 Grade profile (Exit from Toowoomba city to Nass Road junction) 97 6.5 Speed and grade profile of existing road route 98 6.6 Grade profile of existing rail track 100 6.7 Grade profile Postman Ridge to Charlton (new proposed road alignment) 102 6.8 Grade profile near Lockyer to Gowrie (new proposed rail alignment) 104 6.9a B Double Performance Chart (A) 107 6.9b B Double Performance Chart (B) 107 6.10a Six Axles Articulated Truck Performance Chart (A) 108 6.10b Six Axles Articulated Truck Performance Chart (B) 108 6.11 Simulation performance comparison 111 6.12 Fuel performance on new proposed road route 112 6.13 Rail performance: Old rail route versus new rail route 114 6.14 Four options comparison 115 7.1 Intermodal freight movement concept 116 7.2 Road alone freight movement concept 116 7.3 Performance of road vehicles on pick-up and delivery links 120 7.4 Total fuel consumed for scenario one to six 121 7.5 Aggregate fuel performance (Scenario one to scenario six) 122 7.6 Road vehicle performance with 80% payload on 200km road 123 7.7 Train performance in 600km rail link 123 7.8 Efficiency of three train types on 600m rail line haul link 124 7.9 Total fuel consumed in scenario 7 to 12 124 7.10 Energy efficiency between scenario 7 to 12 125 7.11 Total fuel consumed in scenario 13 to 18 126 7.12 Energy efficiency between scenario 13 to 18 126 7.13 Total fuel consumed in scenario 19 to scenario 24 127 7.14 Energy efficiency between scenario 7 and scenario 22 127 7.15 Fuel performance of road vehicle on road line-haul link 128 7.16 Efficiency of road alone haulage 129 7.17 Fuel efficiency for various combinations with Type A Train 129 7.18 Fuel efficiency for various combinations with Type B Train 130 7.19 Fuel efficiency for various combinations with Type C Train 131 8.1 Performance of some simulated cases 135
List of Tables
2.1 Modal Performances by Indicator (out of a maximum 10 points) 9 2.2 External Cost of Rail vs Truck 10 2.3 Locomotive classification and numbers 12 2.4 Types of service and corresponding locomotive class 13 2.5 Energy efficiency of rail locomotives 16 2.6 Road based transport energy efficiency (aggregated) 18 3.1 Energy content of fuel 22 3.2 Factors affecting fuel consumption of heavy commercial vehicles 23 3.3 Factors influencing vehicle energy consumption rate 25 3.4 Factors influencing rail fuel consumption 27 3.5 Cars and light commercial vehicles fuel consumption models 30 3.6 Heavy commercial vehicles fuel consumption models 33 3.7 Rail fuel consumption models 35 4.1 Horizontal curvature adjustment factor 54 4.2 Traffic congestion adjustments to fuel consumption 55 4.3 Classification of road section based on roughness 56 4.4 Adjustment factors 58 4.5 Coefficient contributors 61 4.6 Intermodal transfer energy 66 4.7 Shunting energy demand 66 5.1 Constant values taken for sensitivity analysis of various paramters 73 5.2 Sensitivity summary of various parameters 82 5.3 Constant values taken for sensitivity analysis of various parameters 83 5.4 Sensitivity Comparison 88 6.1 Summary of Road characteristics to the east of Toowoomba 94 6.2 Summary of Warrego Highway characteristics passing through the city 95 6.3 Summary of Warrego Highway characteristics passing through the city 97 6.4 Summary of rail track characteristics 100 6.5 Summary of new proposed second range crossing 103 6.6 Freight type 105 6.7 Comparison table (existing road) 109 6.8 Train consist information 109 6.9 Fuel performance on the existing rail track 110 6.10 Comparison table (proposed road) 113 6.11 New track’s train properties and performance 113 6.12 Four options comparison 115
7.1 Freight routes general characteristics 117 7.2 Alignment properties of hypothetical corridors 117 7.3 Train properties 118 7.4 List of scenarios 119 7.5 Freight moving capacity of scenario one to six 121 8.1 Importance of model parameters on road and rail fuel consumption 134 8.2 Fuel performance on proposed and existing corridors 134
Appendices
Appendix A Commodity classification Appendix B Representative vehicles and their characteristics Appendix C Gradient adjustment factors Appendix D Roughness adjustment factors Appendix E Spreadsheet tool description and users guide Appendix F Spreadsheet Tool – A CD Appendix G A sample result from Vehicle Simulator Run Appendix H Toowoomba Case Study: Proposed Road Alignment Details Appendix I Toowoomba Case Study: Existing Road Alignment Details Appendix J Toowoomba Case Study: Existing Rail Alignment Details Appendix K Toowoomba Case Study: Proposed Rail Alignment Details Appendix L Route alignment details of simulated cases
Acronyms ABS Australian Bureau of Statistics EU European Union GDP Gross Domestic Product GTK Gross Tonnage Kilometers GVM Gross Vehicle Mass HCV Heavy Commercial Vehicle HDM Highway Development and Management IRI International Roughness Index LCV Light Commercial Vehicle NAASRA National Association of Australian State Road Authority NAFTA North American Free Trade Agreement NIMPAC NAASRA Improved Model for Project Assessment and Costing NRM NAASRA Roughness Meter NTK Net Tonnage Kilometers QR Queensland Rail QT Queensland Transport UniSA University of South Australia UoW University of Wollongong VCR Volume to Capacity Ratio VOC Vehicle Operating Cost
Acknowledgements
During this research project, I have received assistance and guidance from many
sources. I would like to express my gratitude to the following:
• My supervisors Professor Luis Ferreira and Dr. Jonathan Bunker for their guidance,
support, patience and many interesting discussions.
• My uncles, Dr. Partha Mani Parajuli, Yogeshwor Parajuli and Sharad Koirala for all
the technical and personal supports.
• School of Civil Engineering for the financial support.
• Dr. Peter Pudney (UniSA), Prof. Philip Laird (UoW) and Mr. Les Bruza (QT) for
their help during model development phase.
• All my friends for their moral support and good discussions.
I am deeply indebted to my aunt Mrs. Reena Parajuli for her support and encouragement
during my study and living in Brisbane. Finally, I thank my mother (Indira Parajuli),
father (Ananta Vijaya Parajuli) and aunt (Urmila Koirala) for all the happiness and pride
they bestow into my life and for the sacrifices that they had to make and for the belief
that they have shown in me.
The errors and inadequacies of the work are the responsibility of the author alone.
Statement of Original Authorship
The work contained in this thesis has not been previously submitted for a degree or
diploma at any other higher education institution. To the best of my knowledge and
belief, the thesis contains no materials previously published or written by another person
except where due reference is made.
Ashis Parajuli
17 November 2005
1
CHAPTER I INTRODUCTION
1.1 Background
The Australian annual freight task is forecast to reach up to 391 billion tonne-km by
2011, an expected rise of about 50 percent (reference year being 2001). The road
freight task alone is projected to exceed 220 billion tonne-km by the year 2011
(BTRE, 2002). Corresponding to this increase in freight task, a rise in energy
consumption can be expected, in spite of energy efficiency gains from vehicle and
engine design improvements.
Energy consumption is directly related to vehicular emissions. Hence, reducing
energy consumption would also benefit the emission reduction program. BTRE
(2002) projected, for business as usual condition, emissions from Australian
transport in year 2020 to be around 68 percent higher than 1990 levels (Kyoto base
level).
Another motivation for energy reduction is to reduce total freight costs. Although
road transport generally is not regarded as energy efficient mode, it has gained a very
large market share of non-bulk freight movement due to the higher reliability and
flexibility. Moreover, for relatively short hauling distances, road dominates the
market (Bunker and Ferreira, 2002).
Several reported energy efficiency studies of freight transport portrays road as one of
the least efficient modes. However, the comparison is generally based only on line-
haul movement does not reflect the overall efficiency of the task. A complete task
may involve more than one mode such as a road legs for pickup and delivery, in the
cases of rail line-hauling.
The need to model energy consumption is closely linked to determining the energy
efficiency of freight movements. Research into energy estimation has been extensive,
2
with some established relationships between fuel consumption of a vehicle and
various parameters influencing energy consumption.
The thesis presents the development and application of an analytical energy
accounting framework to evaluate the performance of available options on land
based freight movement, which is movement involving road and rail.
1.2 Scope
Firstly, the aim of this research was to review the availability of models with the
capability to compare given freight moving options on energy demand (MJ/tonne-
km) basis. From this review, factors to be considered for the proper comparison were
to be determined.
Secondly, the research focused on the development of a methodology and a resulting
framework that could be used to estimate the energy consumption for various freight
movement sections differed by route, traffic and vehicle parameters.
The analytical energy accounting framework developed would be useful to transport
planners in quantifying the energy advantage from mode choice, corridor
development and vehicle types and loading improvements. It is envisaged that the
model can be used as a part of a planning tool to determine the total cost involved in
the freight movement.
1.3 Structure of the thesis
The thesis is structured into eight chapters and twelve Appendices as shown in
Figure 1.1, with a view to providing logical and consistent sequence of information.
3
Figure 1. 1 Structure of the thesis
Literature review
Literature review divided into two chapters (chapter II and III). Chapter II discusses
the trends of freight task and energy consumption, both domestically and
internationally. It also discusses the modes involved in freight movement and vehicle
characteristics. Chapter III discusses the factors affecting energy consumption and
presents the various energy estimation models used for rail and road.
Model development
The fourth chapter presents the model development process and discusses the
spreadsheet tool developed as a part of this research. The main issues addressed in
this chapter are the definition of model requirements and specification, and
estimation procedures.
Chapter II Freight trends: Task and Energy
Chapter I Introduction
Chapter III Estimating modal energy
Chapter IV Model development
Chapter V Sensitivity Analysis
Abstract Table of contents Declaration Acknowledgement
Appendices
Chapter VI Case Study
Chapter VIII Conclusions and Future Research
Chapter VIII Model Application: Simulated Cases
4
Sensitivity analysis
The fifth chapter presents a parametric study of the model. It discusses the
importance of each parameter and the coefficients attached to them. This chapter
discusses the likely error ranges associated with the output of the model developed;
when certain plausible assumptions are made about the measurement errors of the
various independent variables.
Case study and Model Application
The sixth chapter presents a case study carried out as a part of the research. It
includes the application and testing of the developed comparison model. This chapter
presents the energy consumption estimation for four available options and discusses
the advantages and limitations of various options. The seventh chapter includes
model application on some simulated cases. This section illustrates the extended
application of the model on door-to-door fuel consumption estimation and presents
its use in determining the energy efficient option.
Conclusions
The eighth chapter summarises the work described in the thesis, drawing general
conclusions about this specific project and also suggests areas where additional
research is considered beneficial. The chapter also discusses the assumptions and
related limitations. It also recommends the area where further research would be
beneficial.
Appendices
The section contains auxiliary information relevant to the chapters mentioned above.
A CD is included in the appendix which contains a spreadsheet tool developed as a
part of this study to aid in comparing various land based freight moving options. The
appendix also contains the user guide which helps to use the spreadsheet tool.
5
CHAPTER TWO FREIGHT TRENDS: TASK AND ENERGY
2.1 Introduction
The literature review carried out for this thesis is divided into two main areas,
namely: freight movement trends and modal energy consumption estimation. This
chapter deals with the first main area which focuses on the freight modes and growth
trends in both the freight task and the level of energy used in freight transport.
This chapter deals with the following issues:
• The main modes involved in freight movements;
• The growth trends in freight movements; and
• The trends in freight energy consumption and vehicle energy efficiency.
Figure 2.1 shows the structure of this chapter.
Figure 2. 1 Structure of Chapter II
2.1 Introduction
2.2 Freight growth
2.2.1 International
2.2.2 Domestic
2.3 Rail and road freight movements
2.3.1 Bulk freight movements
2.3.2 Non bulk freight movements
2.3.3 Competitive neutrality between road and rail
2.4 Main mode characteristics 2.4.1 Road freight vehicles and units
2.4.2 Rail freight vehicles and units
2.5 Energy in freight: Trends
2.5.1 Energy consumption trends
2.5.2 Energy efficiency trends
6
2.2 Freight growth
2.2.1 International
During the recent past, the world has experienced significant growth in freight
movements. In Europe, an increase of 55 % in tonne-km between 1980 and 1998 has
been recorded, with the largest annual growth in road transport (3.9 % on average)
and short sea shipping (2.6 %), (Trafico and ETCAE, 2001). The growth in land
freight task has been less in Austria and the Netherlands compared to other European
countries. According to Van Arkel et al (2002) the freight task has grown by about
20% in the Netherlands from 1990 to 2000.
European freight transport grew by 70% since 1970, (Communication from the
Commission to the European Parliament and the Council, 1999). This significant
growth of freight task in Europe started to aggravate the road congestion problem.
The annual cost of congestion in the European Union reached 2% of GDP, with road
users accounting for some 90 % of this amount, (EC, 1995).
In Europe, about 2% annual growth is expected in freight transport for the next two
decades (reference year being 1999). If freight transport is not given the proper
consideration, then it might be very costly for Europe to resolve the problems arising
from increased congestion and emissions. (Communication from the Commission to
the European Parliament and the Council, 1999)
Murtishaw and Schippen (2001) noted that the freight task in the US rose from just
over 4000 billion tonne-km in 1988 to over 5000 billion tonne-km in 1998. This is an
increase of about 23% in 10 years or around 2.3% per annum.
North America overall has also experienced an increase in land freight movements.
This has created problems with the movement of goods by truck between the North
American Free Trade Agreement (NAFTA) partner countries. Traffic at land border
crossings has experienced significant growth, particularly along the border between
Texas and Mexico (Steven, 2002).
7
Congestion has a direct impact in the energy efficiency of freight vehicles. Hence,
with a significant increase in congestion, there is an urgent need for mode shift or
corridor infrastructure investment.
2.2.2 Domestic
In Australia, in tonne-km terms, the total domestic freight task has increased over the
past two decades by 58%. Road has increased its share during this time, from 17% in
1974 to 34% in 1993, (ABS, 1997). The total (rail and road) freight task in Australia
amounted to 277 billion tonne-km for year 2000-01.
In Queensland, the freight movement for the year 2001 (year ending on 31 March,
2001) was reported to be 93,416 million tonne-km, (ABS, 2002). Hence
Queensland’s freight task comprised of more than 33% of the whole Australian task.
However, Queensland has only 18.7% percentage of total Australia’s population and
covers 22.5% of total land (2001 Census). Apelbaum (2003) projected the road
freight sector to reached 46,072 million tonne-km by 2000/01, an increment of
around 32% (reference year being 1998/99).
BTRE (2002) projected Australian land freight task to reach up to 391 billion tonne-
km by 2011. This is an expected rise of about 50% (reference year being 2001) in the
coming 10 years. Road freight task alone is projected to exceed 220 billion tonne-km
by the year 2011.
For the projection of Australian freight task and energy consumption, BTRE (2002)
used a ‘bottom-up’ modelling approach across each of the main transport activities.
In this approach, the estimates were made using a summation across major transport
sub-sectors (typically calculated using vehicle fleet models or activity specific
econometric equations). BTRE (2002) argued that bottom-up projection provide
more close estimation because of its ability to cope for increased traffic congestion.
Previously adopted top-down projection approach estimated a slightly higher value
for fuel used. BTRE (2002) highlighted the reason for this slightly higher estimation
by top down model as lack of any constraint parameters to allow for the trend
towards saturation in future.
8
2.3 Rail and road freight movements
2.3.1 Bulk freight movements
In Australia, in the bulk freight movement, rail has a good market share due to its
price competitiveness. Balls et al (2002) reported the dominance of long bulk freight
market by rail. Bulk freight commodities include sugar, coal, steel, minerals, grains
which are usually (but not exclusively) transported in large volume, (Mahoney,
1985).
2.3.2 Non-bulk freight movements
In Australia, due to the higher reliability and flexibility of road freight transport, this
mode has gained a very large market share of non-bulk freight movement. Moreover,
for relatively short hauling distances, road dominates the market (Bunker and
Ferreira, 2002). Earlier Houghton and McRobert (1998) also mentioned an
increasing dominance of road haulage in freight transport and emphasised the need
for intermodal choice and appropriate logistics for better productivity, customer
satisfaction and environmental protection.
Interstate freight movements in Australia have been increasing steadily. Amongst the
total interstate non-bulk freight task, the trend in the shares of all the modes is shown
in Figure 2.2.
Figure 2. 2 The Australia interstate non-bulk freight task: Trends Source BTE (1999)
9
Although the total freight task for both rail and road has been increasing over time,
the relative share of road freight compared to rail has been increasing at a very fast
rate. Road has taken some market share from coastal shipping and it has also
suppressed the growth in rail freight. Until 1983 the non bulk freight shares of road
and rail were almost equal, around 11 billion net tonne-km. However, road transport
has increased its share by more than two times since then.
2.3.3 Competitive neutrality between road and rail
Bunker and Giles (2001) using a survey of decision makers carried out on Brisbane-
Cairns corridor, highlighted the perception of each mode relative to several
performance indicators, such as fuel use, vehicle productivity and freight cost to
operators.
Table 2. 1 Modal Performances by Indicator (out of a maximum 10 points) Source: Bunker and Giles (2001)
The results, which are summarized in Table 2.1, show that road is perceived as
relatively inefficient with respect to energy use when compared with rail and sea.
However the present trends show that road transport is being utilized excessively.
One of the main reasons that the road freight task is growing at the expense of other
modes could be the priority that the road transport is getting from policy makers.
Gargett et al (1999) indicated that current pricing tends to favour road transport over
rail by failing to internalize many costs, shown in Table 2.2.
Jones and Rowat (2003) highlighted the main issues in road and rail pricing, namely;
infrastructure subsidy, uneven tax treatment, and the lack of good data in the relative
pavement damage by different categories of vehicles.
10
Table 2. 2 External Costs of Rail vs Truck (Amounts are in Australian cent per net tonne km) Source: Gargett et al (1999)
In Australia, poor rail track condition has also helped road transport to grow rapidly.
Many previous studies have suggested the improvement of the different tracks for
better rail freight movement. Laird et al (2002) cited twenty-two such research
studies that recommended track improvement as a means to attract more freight to
rail.
2.4 Main mode characteristics
2.4.1 Road Freight vehicles and units
Road based heavy commercial freight vehicles have been classified according to load
the vehicle carries and the vehicle size by various studies, (PMFTS, 2000) and (QT,
2001).
In several studies, vehicles have been categorized into passenger cars, buses, light
commercial vehicles (LCVs), rigid trucks and articulated trucks, (BTCE, 1993),
(Apelbaum, 1998), (Murtishaw and Schippen, 2001), (Ahn et al, 2002), (IFEU and
SGKV, 2002).
LCVs are being used to cater for freight demand in urban areas as heavy commercial
vehicles alone can not fulfil the entire freight task and also due to “just in time”
performance. Laird (2003b) noted the rising trend of LCVs freight task from 0.7
billion tonne-km during 1970-71 to 4.6 billion-tonne-km on 1997-98 for Australian
urban road freight task. Although these vehicles are very competitive in urban
logistics, Dijkstra and Dings (1997) reported that delivery vans have very high
energy use and emissions compared to trucks.
11
Figure 2. 3 Types of combination vehicles Source: QT (2001)
12
Figure 2.3 shows the classification and length restriction imposed by Queensland
Transport (QT) on combination vehicles. In addition, there are other restrictions
imposed by QT for those vehicles to be able to operate. The restriction for load, load
per axle and axle spacing are some of them. Those restrictions depict the concerns
regarding safety and infrastructure damage rather than energy consumption
efficiency and are mentioned in QT (2001).
Sigut (1995) reported on RoadRailer and its use in Australia. RoadRailer is a land
transportation technology combining the main features of road and rail modes. The
modified RoadRailer is hauled on road by a prime mover and on rail by a locomotive
or a modified prime mover.
2.4.2 Rail Freight locomotives and units
In long distance bulk freight movement, rail still dominates the freight task data due
to competitive price advantage.
ABS (2003) divided locomotives into diesel powered and electric powered. These
groups were further subdivided as per their operating system such as on broad gauge,
standard gauge and narrow gauge.
Table 2. 3a Locomotive classification and numbers Source: ABS (2003)
Table 2.3b Wagon classification and numbers Source: ABS (2003)
13
Table 2.3a and 2.3b show the number of locomotives and wagons in the Australian
rail fleet in 2000 and 2001. A large number of the narrow gauge diesel locomotives
are owned by Queensland operators (Queensland Rail and Sugar Cane Railways).
These locomotives service the Brisbane to Cairns route or the extensive rail network
transporting sugar cane. Queensland Rail has the largest fleet of locomotives with
350 narrow gauge diesels and 184 narrow gauge electrics. Other operators with large
locomotive fleets are Freight Corp (NSW) and Tranz Rail (NZ) which operate in
Tasmania, (ABS, 2003).
Hoyt and Levary (1990) classified the locomotives used according to the types of
service for which they are utilized. Table 2.4 shows the classes and their respective
requirements. The locomotives fulfilling the requirements of respective classes could
be grouped into one.
Table 2. 4 Types of service and corresponding locomotive class
Source: Hoyt and Levary (1990)
Lukaszewicz (2001) distinguished the wagons into two categories with respect to
their exposure to the outer environment namely Hbis and Oms. Hbis is a covered
type wagon whereas Oms is an opened type wagon.
Sigut (1995) reported on piggyback technology and its decreasing use in Australia.
Piggyback is a transportation technology using road trailers loaded in flat rail
wagons. The trailers can be modified (with strengthened underframe) or not.
Modified Piggybacks can be lifted by a lifting machine using bottom lift arms, and a
special hitch-wagon provides flexible support during the journey. Unmodified
trailers have to be loaded by pushing with a prime mover over a ramp, and secured to
the wagon by a number of ropes/chains.
14
2.5 Energy in freight: Trends
2.5.1 Energy consumption trends
International trends
In Canada, over the period 1990 to 1999, transportation energy use increased by
20.3% or 380.5 Petajoules. Energy used in freight transport increased by 30.6%
(201.5 Petajoules). The freight transport share of total transport energy use increased
from 35.1% to 38.1 %, (RAC, 2001).
The US also experienced a large growth in transport energy use from 1970 to 1995.
In the same period, freight sector outpaced all other energy consuming sector in
terms of growth in energy use. Vanek and Morlok (2000) noted a 66% increase in
freight energy consumption over the same period.
In Europe there is a lack of data which describe the trend of energy consumption in
the freight sector. EuroStat, one of the largest collectors of those sorts of data, has no
such detailed (split) data available as yet. The total transport energy consumption is
being considered here, Figure 2.4.
Figure 2. 4 Transport Energy Consumption in EU (15 countries)
Source: EC (2002)
As shown in Figure 2.4, the transport sector energy consumption rose steadily
(around 2.3% per annum) over last decade for the fifteen EU countries.
15
Domestic trends
With the rise in freight task in Australia, there is a related rise in energy
consumption. Such an increase in task (58% increment in 20 years) has lead to
significant increase in energy consumption in spite of energy efficiency gains.
Figure 2.5 shows the trend of energy consumption in the Australian freight transport
sector, made up of road, rail and sea modes only.
Figure 2. 5 Freight Energy Consumption in Australia
Source: Laird (2003b)
2.5.2 Energy efficiency trends
For rail transport, several previous studies recommended various energy efficiency
assumptions for different locomotives on different corridors. Table 2.5 shows the
efficiency noted on some of past work in this area.
16
Date Source Description Corridor Energy Efficiency (net-tonne-km/MJ)
1990 Laird and Adorni Braccesi (1993)
Rail freight For super freighters using 81 class 3000 HP locomotives.
Sydney to Melbourne
2
2001 Benjamin and Laird (2001)
NR locomotive
Melbourne to Brisbane
2.84
2002
Affleck (2002)
4000 hp NR Class 2300 Class (supplementary power for steed grades by other)
Australian corridor (Corridor specific values were not revealed.)
5.18 to 8.64 (gross tonne-
km/MJ) *
2002 Rail Freight In Canadian Corridors
4.2
1994/95 Queensland Rail and West Rail
3
2000 BHP iron ore train Pilbara 12 1990s Coal train operation Central
Queensland 5
1999/2000 Adelaide – Perth
2.68
2000 Standard super freighters
Melbourne – Sydney – Brisbane
2.7
1980 Rail freight
Sydney to Melbourne
1.5 to 2
1980
Laird (2003b)
Rail freight
Sydney to Adelaide
3
Table 2. 5 Energy efficiency of rail locomotives
*conversion factor 38.6 MJ per litre
Laird (2003b) noted the improving trend of fuel efficiency on articulated trucks
during the 1990s. For the year 1990/91, articulated vehicles were reported to have
fuel efficiency of 0.82 net tonne-km per MJ. Within the next eight years (to
1998/99), the efficiency rose to 0.95 net tonne-km per MJ. This is a 16% increase in
the average energy efficiency of all articulated trucks.
Figure 2.6 shows the comparison of energy efficiency drawn for different modes of
freight transport in Australia. It depicts that efficiency has been increasing, except for
road transport during 1994-95. The increase in efficiency is also accompanied by the
increase in freight energy consumption as shown in Figure 2.5.
17
Figure 2. 6 Australian domestic freight energy efficiency
Source: Laird (2003b)
However in Queensland, Apelbaum (1998) recorded the rising trend of energy
efficiency on road until 1994/95. Apelbaum (1998) suggested the increase of the
energy efficiency due mainly to the effect of introduction of turbo compounding,
turbo charging, after cooling, computerized engine control system, reduction in
aerodynamic drag and improved drive lines.
ATC (1991), on their study of the energy efficiency of both truck and rail in the US,
suggested the main contributors of improved fuel economy as:
• Locomotive design changes;
• Rail equipment design changes;
• Truck equipment design changes;
• Rail operations changes; and
• Truck operations changes.
Road based transport energy efficiency has also been noted in several past studies.
Slight variations in energy efficiency values of similar modes and categories have
been reported in the literature. Tables 2.6 a and 2.6 b summarize some of the results
reviewed.
18
Vehicle description Specific energy consumption
(litres per NTK) Source
9 axles B Double 0.0162 to 0.1730 6 axles Articulated truck 0.0224 Double road train 0.0092
Affleck (2002)
B Double 0.0280 ACIL(2001) Table 2. 6a Road based transport energy efficiency (aggregated)
Conversion factor: 38.6 MJ per litre of diesel
Specific energy consumption
Vehicle description Urban Non Urban
Source
Light Commercial Vehicle
0.648 lt per NTK 0.648 lt per NTK
Rigid Truck 0.074 lt per NTK 0.076 lt per NTK Articulated Truck 0.037 lt per NTK 0.028 lt per NTK
Apelbaum (1998)
Articulated truck (40 tonne Gross weight) Average load 47%
47.7 lt per 100 km
(0.038 lt / NTK)
Highway 34 lt/100km
(0.028 lt / NTK)
Rural Road 36 lt/100km
(0.029 lt / NTK)
IFEU and SGKV (2002)
Table 2.6 b Road based transport energy efficiency (divided into urban and non-urban)
Conversion factor: 38.6 MJ/ litre of diesel for Apelbaum (1998) and 42.7 MJ/Kg for IFEU and SGKV (2002)
The energy efficiency data reported by ACIL (2001) broadly agrees with the results
shown in Table 2.6 a and Table 2.6 b. For interstate and intrastate rail freight
movements, the same value of efficiency (2.5 tonne-km per MJ) was reported.
Whereas private bulk rail efficiency was reported to be better by more than two times
(6.67 tonne-km per MJ). ACIL (2001) separated the performance of B-Double road
vehicles. The reported efficiency for a B-Double road vehicle is 0.92 tonne-km per
MJ. AGO (2005) also uses Apelbaum Consulting Group energy data for determining
proportion of total consumption of each fuel type by each vehicle type in road
transport.
19
CHAPTER THREE ESTIMATING MODAL ENERGY CONSUMPTION
3.1 Introduction
This chapter deals with the second main area of the literature review which provides
an understanding of energy consumed by road and rail transport.
The following issues are explored at this stage so as to aid in the development of a
modal energy consumption model:
• The parameters governing the energy consumption and efficiency for each
main land transport mode;
• Existing modal energy consumption models;
• The parameters that need consideration while comparing the energy
consumption between road and rail transport; and
• Past works on energy comparative methodologies.
Figure 3.1 shows the structure of this chapter.
Figure 3. 1 Structure of Chapter III
3.1 Introduction
3.2 Factors affecting fuel consumption
3.3 Energy consumption models
3.2.1 Fuel and energy content
3.2.2 Road transport
3.2.3 Rail transport
3.5 Conclusions and implications
3.4.3 Factors influencing comparative studies
3.4.4 Limitations of comparative studies
3.4 Energy consumption:
Comparative studies
3.4.1 Introduction to intermodal transport
3.4.2 Previous comparative methodologies
3.3.1 Road transport
3.3.2 Rail transport
3.5.1 Conclusions from the literature review
3.5.2 Implications for the thesis
20
3.2 Factors affecting energy consumption
Vehicle type is not the only parameter that affects modal energy consumption and
energy efficiency. There are several other parameters that need to be addressed. For a
better understanding of fuel consumption, firstly the energy content of the fuel is
discussed. Energy consumption influencing parameters for road and rail modes are
addressed later in this section.
3.2.1 Fuel and energy content
Fuel consumption is expressed in volume per travelled distance, and is therefore
influenced by the energy content of the fuel. For a given thermal efficiency of the
engine, the fuel consumption is lower when the energy contained in a litre of fuel is
higher.
Among the different sources of energy used in Australian freight transport, the
energy produced by fossil fuels is expected to dominate. BTRE (2002) assumed
diesel to be practically the only source of motive power for articulated trucks to
2020. Similarly, for rigid trucks diesel is assumed to be the primary fuel with its
share increasing to 95% in 2020. For rail (including passenger train), diesel oil has
dominated the energy supplied. 25.29 PJ energy was derived from diesel in year
2000 compared to 6.42 PJ using electricity, (BTRE, 2002). ABS (2003) reported the
existence of 2035 diesel locomotives in operation compare to 265 electric
locomotives (Table 2.3a), which strengthen the fact that still diesel power is driving
the large portion of rail transport. BTRE (2002) projected the rise of diesel utilization
up to 37.63 PJ by the year 2020 compared to 8.12 PJ in electricity share.
ABARE (1993) reported on the energy content of different kind of fuels. The values
reported, which are indicative only, are the gross energy content of the fuel – that is;
the total amount of heat that will be released by combustion at 15°C and 1
atmospheric pressure. The gross energy content of the Automotive Diesel Oil (ADO)
has been listed as 38.6 MJ per litre, (ABARE, 1993). Affleck (2002) also adopted the
same 38.6 MJ per litre of diesel as the conversion factor of diesel fuel into energy, as
did Laird and Adorni-Braccessi (1993).
21
IFEU and SGKV (2002) in their energy comparison of different locomotives in
Europe took 42.7 MJ / kg as the energy content of diesel. Wood et al (1981) opted
for 42.84 MJ / kg as the gross energy content of diesel to study the energy
consumption by various types of vehicles for the UK conditions. Similarly Wang
(2000) reported the energy content being 41.7 GJ per tonne of diesel (35.7 GJ per
cubic meter) which is equivalent to 41.7 MJ / kg. Whereas Shayler et al (1999) used
the energy content of the diesel fuel as 44 MJ / kg.
ABARE (1993) has expressed specific volume of ADO as 1182 litre per tonne. That
means ABARE (1993) recommended 45.63 MJ / kg of diesel as compare to 42.7 MJ
/ kg that IFEU and SGKV (2002) used in their study for comparing energy
consumption. AGO (2005) also reported emission factors relating to energy
consumption in the Gross Calorific Value (GCV) to keep it in accordance with
ABARE reports.
Wood et al (1981) used primary energy equivalent of diesel as 11.11 kWh per litre.
This exceeds the primary energy equivalent that could be derived from Laird and
Adorni-Braccessi (1993) by 0.39 kWh per litre. Considering Laird and Adorni-
Braccessi (1993)’s conversion factor of 38.6 MJ per litre of diesel and 1 kWh per 3.6
MJ, the primary energy of diesel is 10.72 kWh per litre.
Slight variations in the energy content of diesel could be observed across the
literature. IFEU and SGKV (2002) explained this variation as the cause of
differences in extracting and refining procedures at various locations. This difference
in procedure could also result in differences in carbon content and sulphur content of
the fuel. This variation of carbon content is expected to have an impact on the gross
energy content of the fuel.
In Australia, coal is the main source of fuel for the generation of electricity. ABARE
(1993) noted that coal accounted for 41.8% of total energy consumption in 1991-92,
but only 4.6% of final energy consumption. This marked difference is due to coal
mainly being used in conversion processes, particularly electricity generation.
22
Energy Content (MJ / kg) Source Diesel 42.84 Wood et al (1981) Diesel 45.63 ABARE (1993) Diesel 41.70 Wang (2000) Diesel 42.70 IFEU and SGKV (2002) Diesel 44.00 Shayler et al (1999) Black Coal 27.00 ABARE (1993) Table 3. 1 Energy Content of fuel
Concawe (1999) reported that density and heating value are the two relevant fuel
properties of diesel, but these values alone have no effect on the thermal efficiency
and do not induce energy savings.
A term called cetane number (CN) was introduced while describing petroleum
product’s quality and burning tendency on different types of engine, Kagami et al
(1984) and Patel (1999).
Patel (1999) reported that CN expresses the ignition quality of fuel. The higher the
CN, the shorter the ignition delay period leading to lower rates of pressure rise and
allowing improved control of combustion.
Viscosity of liquid fuel has been considered as another parameter governing fuel
quality for energy content. Kagami et al (1984) noted viscosity and its impact on
specific fuel consumption and emissions. The specific fuel consumption (km per
litre) was noted to have risen until the viscosity reached 5 mm2 per sec limits.
Specific fuel consumption started to decrease slightly as the velocity rose beyond 6
mm2 per sec.
BT (1995) mentioned the effect of low Sulphur diesel on fuel efficiency of heavy
commercial vehicles. It raised doubts that the clean diesel and emission control
technologies might bring a negative impact on the fuel efficiency of heavy
commercial vehicles.
23
3.2.2 Road transport
William (1977) reported the factors affecting the fuel consumption of heavy
commercial vehicles. The factors were categorized according to their relative impact
on fuel consumption.
Table 3. 2 Factors affecting fuel consumption of heavy commercial vehicles
Source: William (1977)
Essenhigh et al (1979) studied the variation of automobile fuel consumption with
respect to vehicle size and engine displacement. The study concluded that the effect
of weight on fuel consumption is much more complex than a simple linear
correlation between specific fuel consumption and weight would imply. However,
Ghojel and Watson (1995) gave two separate relationships (one for an urban cycle
and other for a highway cycle), describing the linear variation of specific fuel
consumption of heavy vehicles with respect to vehicle mass. Those relations were
reported to have correlation coefficient (R2) of 0.936 and 0.938 respectively. The
relationship developed by Thoresen (1993) did not provide such a good fit and was
developed from the freight vehicle operating cost survey which contained a small of
number of heavy commercial vehicles. Similarly, Houghton and McRobert (1998), in
comparative study of resource consumption, assumed the linear variation in fuel
consumption with respect to gross vehicle mass.
24
Other studies, such as Biggs (1988), Bowyer et al (1985), Biggs (1987) and Post et al
(1984) categorized the fuel consumption influencing parameters as:
• Rolling resistance
• Aerodynamic resistance
• Inertial forces
• Grade force
• Cornering resistance
• Drive-train efficiency
• Power required for vehicle accessory
Greenwood and Bennett (2001) presented a flow diagram showing above factors and
their relative energy utilization, as shown in Figure 3.2. Those authors argued that
only 18 percent of the total energy in the fuel is available to propel the vehicle along
the road under typical urban driving conditions.
Figure 3. 2 Energy Train for a typical urban car
Source: Adopted from Greenwood and Bennett (2001)
BT (1995) noted major fuel consumption influencing parameters as the age and type
of vehicles in operation, condition of the equipment and standards for maintenance
and repair, technologies used, terrain travelled and driver's skill.
Ahn et al (2002), in a study on energy consumption patterns of cars and light
commercial vehicles, categorised the variables influencing vehicle energy rates into
six broad groups.
25
Category Factors
Travel related Distance between two terminals, number of trips etc Weather Related Temperature, humidity, wind effects etc Vehicle related Engine size, the condition of engine, equipments in the vehicles
such as AC, catalytic converter etc Roadway related Road grade, surface roughness, etc Traffic related Vehicle to vehicle interaction and vehicle to control interaction Driver related Driver behaviour and aggressiveness Table 3. 3 Factors influencing vehicle energy consumption rate
Source: Ahn et al (2002)
There are various small additional fuel consumption needs to be fulfilled, such as
those due to evaporation loss (EC, 1999); cold start (Chang et al, 1976); tyre pressure
variation increasing the rolling resistance; and small fluctuations of speed (Biggs,
1988) and (BT, 1995).
3.2.3 Rail transport
Meibom (2001) illustrated different operating phases of any vehicle and described
the fuel consumption requirement of each phase.
Figure 3. 3 Model of energy flow in vehicle
Source : Meibom (2001)
Figure 3.3 could be used to study the energy consumption influencing factors. The
losses of fuel through the energy storage unit, such in the form of evaporative losses,
influence the final energy consumption of a vehicle. Factors such as type of engine
26
and motors would have a higher impact on energy consumption, as those factors
govern the type of fuel required and energy conversion efficiency. The losses during
transmission also have an influence on the final energy consumption. Since the major
portion of energy is utilized on tractive force, the latter is an important parameters
governing energy consumption. In addition, the energy requirement for running other
accessory functions, such as air conditioning, lighting, etc., also influence the final
energy consumption.
IFEU and SGKV (2002) addressed the following factors as important parameters to
be considered for rail fuel estimation:
• Traction type (diesel or electric);
• Train length and total weight;
• Ratio of gross to tare mass of train and unit load devices;
• Route characteristics (gradient, curvature); and
• Driving behaviour (speed, acceleration) and air resistance.
Lukaszewicz (2001) derived an energy consumption estimating relation with
assumptions that the energy consumption of a train varies with:
• Track parameters such as radius of curvature, rail pads (e.g., hard rubber,
steel, soft rubber), track type (e.g., continuous welded, jointed, etc), ballast
and grade.
• Mechanical and physical parameters such as wheel radius, gear ratio, engine
conversion efficiency, length and face of train and type of wagon.
• Operating conditions such as velocity, acceleration, load and rotational
inertia.
• External factors such as wind and climate, which might affect slippage ratio
along with other factors relating to track and vehicle; and
• Driver’s behaviour.
EC (1999) considered steady load, velocity and number of stops as significant
parameters that could describe the energy consumption of train. Earlier Jorgenson
and Sorenson (1998) had used a similar approach to estimate rail fuel consumption.
27
Hoyt and Levary (1990) grouped the factors influencing rail transport fuel
consumption as train characteristics, terrain characteristics and other unpredictable
variables, as shown in Table 3.4.
Table 3. 4 Factors influencing rail fuel consumption
Source: Hoyt and Levary (1990)
Type of track, wagon, velocity, number of stops and driver’s behaviour were not
mentioned by Hoyt and Levary (1990). These factors are listed on other studies, such
as EC (1999) and Lukaszewicz (2001), as parameters affecting rail fuel consumption.
3.3 Energy consumption models
3.3.1 Road transport
Passenger cars and light commercial vehicles (LCV)
During the 1970’s, the energy consumption of cars and LCVs were estimated using
regression models using speed as the single most important independent variable.
Chang et al (1976) used distance between links and travel time for fuel consumption
estimation. This type of average speed model continues to be used due to its
simplicity and acceptable accuracy. Bowyer et al (1985) and Biggs (1988) also used
the average speed formulation along with other models. To better describe the fuel
estimation, the terms such as rise, fall and roughness were introduced in those
regression (empirical) models. Greenwood and Bennett (2001) reported the form of
those equations as:
Fuel consumption = a0 + a1/v + a2 v2 + a3*RISE + a4*FALL + a5*ROUGHNESS
Post et al (1984) compared the results of a more complex power demand model with
the simple average speed model and concluded that both give similar results and
accuracy for longer distance trips. Bowyer et al (1985) stated satisfactory
28
performance of the average speed model on a long distance provided average travel
speeds are not high.
In the 1980’s, advances in fuel consumption modelling leaded to the incorporation of
various other parameters. Post et al (1984) developed a relationship between fuel
consumption and power developed at the vehicle’s tail shaft. The tail shaft power
(Ztot) represented a summation of drag power, inertial power and gradient power.
Two constant terms were introduced representing the idle fuel consumption and
efficiency.
FC (ml/min) = α + β Ztot for Ztot ≥ 0 kW
= α (ml/min) for Ztot < 0 kW
Ferreira (1985) developed an empirical relation for estimating urban fuel
consumption using data from Leeds, the UK. The fuel consumption influencing
factors such as stop/start and slowing down was incorporated in that model.
Bowyer et al (1985) classified different types of models into four groups, namely,
• Average speed model;
• Running speed model;
• Four mode elemental model; and
• Instantaneous model.
The shortcomings of average speed models, such as its inability to differentiate fuel
consumption during the running and idle phase, led to the development of running
speed model. Running speed fuel consumption model incorporates the average effect
of grade, effect of difference in fuel consumption while running and idle. Bowyer et
al (1985) reported that this model could underestimate the fuel consumption over a
trip and the error was related to the grade term.
Further moves towards accurately estimating the energy consumption led to the
development of the four mode elemental model. This type of model was also
reported by Akcelik (1983). Bowyer (1985) presented a refined form of the same
model, which estimates fuel consumption by classifying a vehicle operation into four
phases, namely: idle, cruise, acceleration and deceleration. As reported by Post et al
(1984), average and running speed models can not estimate energy consumption well
for short section (less than 5 km) whereas four mode elemental model could be used.
29
Instantaneous model resembles the model developed by Post et al (1984). These
models explain fuel consumption in small time increments. The use of instantaneous
model on long road section is less likely to improve the energy estimation result and
it only increases the complexity of calculation. Bowyer et al (1985) and Post et al
(1984) suggested the good performance of previously mentioned simpler (average
speed and regression) model over instantaneous and four mode elemental models
when it comes to longer trip distances.
For specific and more accurate calculation of fuel consumption, relations based on
mechanical performance of the engine have been developed. Shayler et al (1999)
developed such a model and predicted fuel consumption using characteristic
relationships of engine. Specific fuel consumption estimation was based on gross
indicated power which is related to compression ratio and spark timing.
In recent times, there is the emergence of data-based models as developed by West et
al (1997), who tested the vehicle on road and on dynamometer to establish
relationship between fuel consumption, vehicle speed and acceleration. Ahn et al
(2002) developed a regression model that uses instantaneous speed and acceleration
to estimate energy and emissions. Unlike West et al (1997), the model was divided
into two relations so as to correlate the vehicle fuel performance with change in the
nature of acceleration (positive and negative). This change in the acceleration
induced a different set of regression coefficients.
Tong et al (2000) studied the fuel consumption of the light duty petrol and diesel
van, petrol passenger car and double-decker public bus. The relationship between
instantaneous speed and fuel consumption was established for each vehicle class.
A summary of fuel consumption models reviewed is presented in Table 3.5.
30
Table 3. 5 Cars and light commercial vehicles fuel consumption models
Heavy commercial vehicles
Biggs (1988) extended the work of Bowyer et al (1985) to include heavy commercial
vehicles (up to 40 tonne articulated trucks). The set of models, known as ARFCOM,
used three categories, namely; instantaneous fuel consumption model, elemental
model and running speed model. Figure 3.4 shows the approach that ARFCOM used
for modelling fuel consumption.
31
Figure 3. 4 ARFCOM approach to modelling fuel consumption
Source: Biggs (1988)
The developed models incorporate various power components such as power needed
to overcome rolling resistance, aerodynamic resistance, inertial force, grade force,
cornering resistance and power needed for vehicle’s other accessories. HDM-4
(Highway Development and Management) took the ARFCOM model as a basis to
quantify the fuel consumption as a part of estimating vehicle operating cost,
(Greenwood and Bennett, 2001).
Instantaneous fuel consumption models are well suited to congested traffic
conditions. However, such models require a high computational effort and the
vehicle must be coupled with microscopic traffic simulators. For estimating fuel
consumption of commercial vehicles at the corridor level, the running speed sub-
model is likely to be appropriate. Some precautions are necessary for using the
models to allow for fuel estimation during negative power demand phase, frequent
change in vehicle parameters and underestimation of the grade effect. The effect of
these shortcomings could be reduced by dividing the road length into homogeneous
sections and recalibrating the model for new vehicle parameters. Since these
mechanistic models do not have a variable describing speed-smoothness explicitly,
they are insensitive to small changes in traffic conditions. Thoresen and Roper
(1996) suggested the necessity of further research to validate ARFCOM roughness
estimate since the effect of speed variability associated with higher roughness values
are not catered for.
32
Kent and Mudford (1979) suggested a different approach which uses the carbon
balance method to estimate fuel consumption. EC (1999) also suggested a similar
method for commercial vehicle fuel consumption estimation. Kent and Mudford
(1979) suggested the use of two different phases namely cruising and non cruising
phase, having different emission relations, whereas EC (1999) did not make the
differentiation. In the case of Kent and Mudford (1979), the suggested emission
relation was a function of speed and acceleration, not speed alone as for EC (1999).
Energy consumption models having speed as the only influencing parameter have
been used for sometime. Tomita (1997) used a speed regression model developed by
Adachi, Mori and Fujushiro in 1984. The developed relations were a polynomial
function of speed.
Meibom (2001) suggested a more complex model that estimates the energy
consumption per driving cycle as a function of tractive force used to overcome air
resistance, rolling resistance, difference in potential energy, energy needed for
auxiliary purposes, load, average transmission efficiency and thermal efficiency. The
difficulty in the application of the model is likely where there are large vehicle
categories with different route parameters. Wang et al (1992) also reported an
analytical model for energy consumption which dealt with mechanics of the vehicle
system and evaluated the motion phenomenon of the system. The developed
analytical model estimates the energy requirement over a cycle for a given vehicle
and driving cycle.
Other approach includes estimating fuel consumption as a function of number of
cylinders (z), engine speed (n) and fuel injected to the engine (∂) at every instance
(Sandberg 2001).
∫ ∂=
end
o
t
t
dtznfuelm )60000/1*2/**(
A summary of fuel consumption models reviewed is presented in Table 3.6.
33
Model type Comments References
Regression Various studies developed a correlation between energy consumption and fuel consumption influencing factors.
Tomita (1997)
Running speed Divide the operation of a vehicle into two phases; run and idle. Have little room to incorporate the effect of grade and inertial power. High chance of underestimating the effect of grade on a long trip.
Biggs (1988)
Four mode elemental
Divide the vehicle’s operation into four phases namely idle, cruise, acceleration and deceleration. Could be used for short trips.
Biggs (1988)
Instantaneous Estimate the fuel consumption for a small increment in time and length. These types of models include a large set of input parameters.
Biggs (1988)
Based on emission and carbon balance method
Carbon emission from the vehicle was correlated with speed and then later the carbon balance method was used for estimating fuel consumption. Have speed as the only energy consumption influencing parameter, other factors should be covered by the coefficients.
EC (1999)
Wang et al (1992) Analytical Estimates the energy required over a cycle for a given vehicle and driving cycle. Meibom (2001)
Table 3. 6 Heavy commercial vehicles fuel consumption models 3.3.2 Rail transport
Kraay et al (1991) developed an energy consumption model based on energy needed
to overcome resistance along with an energy parameter related to change in kinetic
energy. The resistance term in the relation accounted for grade, radius of curvature,
mass of train, air friction, rail friction and speed. Different coefficients were adopted
for correlating those terms with energy consumption which would depend on train
and track type.
The energy consumption of a train has been estimated using speed as a prime
influencing factor. EC (1999) suggested a function of average speed and distance
between stops for train energy estimation. EC (1999) and Sorenson (1998) present
values of the constants determined after calibrating such models on particular
corridor and locomotives.
34
Energy consumption (KJ / tonne– km) = k * Vavg2 / ln(x) + C
where:
k and C are train dependent constants; x is the distance between stops in km; Vavg is
the average speed over the section of the route under consideration in km/hr.
A similar model was suggested by Jorgenson et al (1998) for typical German ICE
trains (with the values of k and c being 0.007 and 74 respectively). In those models,
the effect of grade is supposed to be incorporated by average speed as the effect of
grade would be to reduce the average speed.
EC (1999) also suggested another method for train energy estimation, which is based
on steady state loading of the train. Steady state train loads in kN were converted to
kJ/tonne-km for several types of trains and were found to have a second order
dependence on train speed due to aerodynamic loading.
The integrated energy consumption for a train over a given route was ultimately
expressed as a function of number of stops (NSTOP), change in elevation (∆h),
average and maximum speeds to which the train accelerates.
E = (NSTOP + 1) / L * V2max / 2 + B0 + B1 * Vavg + B2 * V2
avg + g * ∆h/L
where:
B0, B1 and B2 are empirical coefficients for the steady state load.
The model was found to produce good results where:
• there are less number of stops; and
• the acceleration and deceleration process are not that frequent.
The model was found difficult to apply where:
• there are significant changes in the variables. There is a need to separate the
route into homogeneous sections in presence of large variable set.
• there are difficulties in determining the true numbers of accelerations and
Vmax that might occur because of traffic conditions. This affects the first term
of the equation by underestimating acceleration energy consumption.
35
Lukaszewicz (2001) expressed train energy consumption as a function of tractive
force at wheel, velocity, acceleration, slippage ratio, efficiency of the conversion and
the locomotive’s mechanical and physical parameters such as wheel radius and gear
ratio. Separate relationships were obtained explaining the energy demand during
coasting and non coasting phase. This approach could be suitable to compare the
efficiency of different types of trains and track.
IFEU and SGKV (2002) used a very simplistic approach for an energy consumption
comparative study. Specific energy consumption per train-km (ECtrain, in wh / km)
was calculated as a function of gross weight of train (Mtrain, in tonne).
ECtrain = 315 * Mtrain0.6
This model lacks a proper description of other fuel consumption influencing
parameters such as slope, slippage, track curvature and speed variations.
At the corridor level, it would be prudent to consider the steady state loading type
model suggested by EC (1999), or the model suggested Kaary et al (1991), since
these models were reported to have satisfactory performance when there were fewer
stops and tracks were easily divided into homogenous sections.
Table 3.7 summarises models reviewed here.
Model type Comments References Kraay et al (1991) Power demand Estimates the energy required based on the
power needed to overcome resistances and change in kinetic energy. Lukaszewicz
(2001) EC(1999)
Jorgenson et al (1998)
Regression Various relations have been established between energy consumption by train and their operation parameters such as velocity, number of stops, mass etc.
IFEU and SGKV (2002)
Table 3. 7 Rail fuel consumption models
3.4 Energy consumption: Comparative studies
Several Australian Railway Association (ARA) rail fact sheets argue that rail freight
transport consumes much less energy than road transport. However rail alone cannot
fulfil the entire responsibility of freight task. Hence for energy consumption
comparison the concept of comparing the intermodal land transport with road
36
transport seems to provide more comprehensive and acceptable results than a single
mode comparison. To better understand the door to door energy consumption, this
section has been subdivided into introduction to intermodal transport, previous
comparative methodology, factors affecting comparative results and limitations of
previous studies.
3.4.1 Introduction to Intermodal transport
Mahoney (1985) described intermodal transfer as a transfer of commodities or goods
between two modes. Mahoney (1985) also emphasised containerization and
intermodality as not being synonymous but the use of containers compatible with
two or more modes greatly improved intermodal transfer of general cargo. To
provide a seamless intermodal transfer between road and rail, the units such as piggy
back, roadrailer, swap bodies have been in use (Mahoney, 1985 and Robl, 2002).
There has been a significant improvement in the intermodal technology over the
years. This could be confirmed by the technologies described in Mahoney (1985),
Sigut (1995) and Robl (2002). Intermodal movement can be defined as the
movement of goods in one and the same loading unit or road vehicle, which uses
successively two or more modes of transport without handling the goods themselves
in changing modes. However, for the purpose of this study, intermodal transport is
defined as a system of moving goods from origin to destination which involves road
and rail.
IFEU and SGKV (2002) and Affleck (2002) compared the door to door energy
consumption between road and intermodal transport. Both the studies found an
energy advantage of intermodal transport in most cases. The development of
seamless intermodal facility could be expected to further enhance the energy
advantage and smooth freight movement.
Different equipment is used in intermodal transfers phases such as move, stack and
load-unload. Robl (2002) classified these lifting equipment according to the position
from which they lift the trailers, namely bottom pick and top pick. All these
37
operation phases of intermodal transfer consume energy and the magnitude differs
from adopted system and technology.
Figure 3. 5 Intermodal transfer of various carriage units
Source: www.freightcommercial.co.uk; accessed on August 4, 2003.
3.4.2 Previous comparative methodologies
IFEU and SGKV (2002) carried out an energy consumption comparative study to
confirm the validity of the argument that shifting the freight load from road to rail
would significantly reduce the energy consumption and greenhouse gas emissions.
The study examined the energy consumption and greenhouse emissions in nineteen
corridors in western and central Europe.
They used average specific energy consumption data of trucks for different road
types to estimate the fuel consumption for a total trip. The TREMOD model was
used for quantifying the influence of load factor which estimates the fuel
consumption for empty run to be as low as 2/3 of the fuel consumption of the fully
38
loaded value. For quantifying the grade influence multiplying factors such as 3.7, 0.3
and 1.5 were used for upgrade, downgrade and average grade respectively.
Similarly, for estimating energy of rail transport, a specific fuel consumption relation
was used as discussed in section 3.3.2. To incorporate the difference in the specific
energy consumption due to the difference in mass of certain wagon, the train was
first compared with the reference set. Then the difference in those two set was
adjusted to the considered wagon (unit) for finding the specific energy consumption
rate of that unit.
IFEU and SGKV (2002) considered the energy consumption in intermodal transfer
phase and concluded that in the whole comparison process the energy consumed in
these cycles are insignificant even for the shorter distance of 100 km (less than 3%).
When energy consumed for shunting was combined with intermodal transfer phase,
the significance did not rise by much. For the whole comparison process, IFEU and
SGKV (2002) opted to omit the effect of shunting and intermodal transfer on energy
consumption.
Following IFEU and SGKV (2002) conclusion, Affleck (2002) opted to exclude the
energy consumed in shunting and intermodal transfer in their comparative study
carried out in seven freight corridors in Australia. Affleck (2002) based their study
on the methodology suggested by IFEU and SGKV (2002). Hence, there is very little
difference in the comparison methodology between those two studies, except for
train fuel consumption estimation.
Unlike IFEU and SGKV (2002), Affleck (2002) used corridor specific in service fuel
consumption rates to calculate the locomotive fuel consumption. For truck fuel
consumption, both studies used the actual fuel consumption rates collected from
various sources. Affleck (2002) adopted typical pick up and delivery distance as
suggested by road freight operators.
Haferkorn (2002) compared the performance of truck and various types of freight
trains. For a fair comparison, all the vehicles were loaded with the same containers
(40 ft sea containers) and a payload of 30 tonne. Values for air and rolling resistance
39
were adopted from the literature. The vehicles performance was measured for
transporting 1 million tonne-km per hour. This method is not related to energy
consumption alone. The method induces a cost indicator which explains and prepares
a base for comparison involving cost factors such as transport operating and
depreciation costs.
Dijkstra and Dings (1997) compared the specific energy consumption and emissions
of freight transport between road, water, rail and air. The comparison was focused on
specific energy consumption of those modes per effective kilometres (straight line
distance between two points). However the detour factors would vary with location
and plays a major role in the final energy consumption comparison. The average load
factor and maximum load were also acknowledged as major characteristics of the
freight transport modes. For the comparison, energy consumption data were collected
and analysed to obtain the average specific energy consumption data by classified
vehicle class. The comparison was limited to the mode level rather than energy
consumed for door to door service.
Another similar comparative study was carried out by ATC (1991). However, unlike
other studies this study used computer simulation for truck and rail energy
calculation (Vehicle Mission Simulator (VMS) for truck and Train Performance
Simulator (TPS) for train). The study also considered specific routes, loads, and
equipment. The results were determined along real transportation corridors like IFEU
and SGKV (2002) and Affleck (2002). The results include calculations for fuel used
in local rail switching and terminal operations. Rail demonstrated better fuel
efficiency for all combinations of vehicle, from 1.4 to 9 times better than trucking.
However, ATC (1991) noted the decrease in the relative advantage of rail compared
to truck due to circuitous route. In addition, the report describes changes in the
design and operations of both rail and truck transport and attempts to quantify the
impact on fuel efficiency of each.
Houghton and McRobert (1998) developed a worksheet model to compare resource
consumption. The comparison lacks the proper explanation of pickup and delivery
energy consumption and the variations in representative vehicle classes are limited.
40
3.4.3 Factors influencing comparative studies
IFEU and SGKV (2002) noted some influencing factors that need consideration for a
comprehensive comparison of combined transport rail/road versus road transport.
Figure 3.6 summarizes these factors.
Figure 3. 6 Factors influencing a comparative study
Source: IFEU and SGKV (2002)
IFEU and SGKV (2002) noted the factors such as energy supply mix, as a major
influencing factor in comparative energy consumption. If electricity is used, about
2/3 (depending upon the input mix) of the supplied energy could be used for
conversion and the upstream process steps (such as extraction and processing of fuels
for electricity generation). Whereas for diesel fuel, the final energy use contributes
41
about 90% of the total primary energy demand. Similarly, load factor was also given
a high importance along with distance. For determining the load factor variation, the
TREMOD model was used. The factors such as return trips, characteristics of freight
and logistics were also expected to have an influence in the whole comparison
process. Shunting, intermodal transfer and grade were given very less priority.
3.4.4 Limitations of comparative studies
• IFEU and SGKV (2002) used specific fuel consumption rates collected from
various sources, to describe the fuel consumption of heavy commercial vehicles.
This approach would limit the outcome to the corridor levels and would be
difficult to transfer the results.
• IFEU and SGKV (2002) assumed that the grade effect for rail locomotives would
be described by the extra weight that the added locomotive would induce for
dragging the train at grade. No data was provided to support this assumption.
• IFEU and SGKV (2002) categorized the road vehicle’s fuel consumption based
on urban, non urban and rural cycle. The classification ignores other traffic and
road related parameters.
• There are various vehicle categories that need to be considered such as LCV,
rigid truck and articulated truck. The differentiation of the types of vehicles on
the pickup and delivery legs could bring more understanding of the ultimate fuel
consumption.
• As Affleck (2002) used the similar methodology, the study inherits the same
limitation that of IFEU and SGKV (2002). Moreover Affleck (2002) used
corridor specific fuel consumption data for both road and rail unlike IFEU and
SGKV (2002) that used those data for road transport only. However, ATC (1991)
used computer simulations which are not reviewed here. ATC (1991) mentioned
that for truck simulation only one truck engine was selected, that is the Cummins
350.
• The energy consumption of empty back hauls was not considered in either of the
comparative studies reviewed.
• Commodities were not classified in Affleck (2002) and IFEU and SGKV (2002).
ATC (1991) and Affleck (2002) used tonne-km to represent freight task. Hence
42
these studies carry the deficiency of tonne-km method (as mentioned on BT
(1995)), such as not being able to represent cubic volume and speed.
• ATC (1991) assumed that truck freight moves directly from origin to destination.
However, there is a possibility of involvement of different freight vehicles for
pickup and delivery legs when freight is being carried by long vehicle on road
(Houghton and McRobert, 1998).
3.5 Conclusions and implications
3.5.1 Conclusions from the literature review
Among the various modes of freight movement, land freight movement modes were
studied in detail for understanding their energy utilization characteristics and the
main influencing factors. The growth of road freight movement has been found to be
significant compared to rail. Several previous studies suggested rail as an energy
efficient mode when measured on tonne-km per fuel consumption basis.
The review looked at ways of estimating energy consumption of a complete freight
task i.e., from origin to destination. Models developed for estimating the energy
consumption for rail, heavy commercial vehicles and light commercial vehicles were
reviewed. Approaches and methods used previously to establish the relationship
between energy consumption influencing parameter and fuel consumption were also
studied, so as to aid in developing the relationship during model development. For
road fuel consumption estimation, models have been divided into instantaneous, four
mode elemental, running speed and average speed, and carbon balance. For rail fuel
estimation no such hierarchy of fuel consumption model has been established. The
models reviewed are grouped as mechanistic (or power demand) and regression
models.
The latest methodologies adopted in freight modal energy comparison were studied.
It has been acknowledged that for the comparison purposes, some additional
parameters such as trips length and regulatory constrains also need consideration.
The review supports the homogeneous division of road section and grouping of like
vehicles for increasing the accuracy of estimation. The review suggests the grouping
43
of road sections as per terrain and traffic characteristics and vehicles as per load they
are carrying, number of tyres and axles, power and engine capacity.
3.5.2 Implications for the thesis
There are numerous studies carried out for quantifying the fuel consumption of rail
and road modes. However, there is a lack of a proper methodology for comparing the
fuel consumption performance of rail and road on Australian corridors. Studies such
as Affleck (2002) started such comparisons, however, the results are corridor specific
and cannot be implemented for other corridors. Hence, this research is focused on
comparing the fuel consumption of rail and road taking into account the presence of
various traffic and terrain characteristics. In this way, a general model may be
implemented for application to various corridors within and outside Australia.
Various fuel consumption influencing factors that are of importance to this study
have been identified (Section 3.2 and section 3.4.3). Some of the important factors to
be considered in this study are:
• Pay load
• Gradient
• Speed
• Roughness
• Vehicle category
• Road type and congestion
The energy contained in a litre of diesel is taken as constant (38.6 MJ per litre) in
spite of slight variations.
Figure 3.7 highlights the route and transport characteristics that were compared for
the proper understanding of the energy consumption of a freight vehicle from origin
to destination. This study focuses on quantifying the total energy consumption on
each of those routes.
44
Figure 3. 7 Comparison routes
Road leg (delivery)
Could be LCV or rigid truck
Could be LCV or rigid truck
or articulated truck
Road leg (delivery)
Road leg (pick up)
Could be LCV or rigid truck or articulated truck
Road leg (pick up)
Could be LCV or rigid truck
Origin
Collection Collection
Collection Collection
Destination
Total haulage by
Road (by rigid or articulated
truck)
Road line haul
(by rigid or articulated
truck)
Rail line haul
45
CHAPTER IV MODEL DEVELOPMENT
4.1 Introduction
This chapter describes the development of a tool to undertake energy consumption
modal comparisons between land based freight modes. The main issues addressed
here are: the definition of model requirements and specification; estimation
procedures and model calibration and validation.
A graphical depiction of the comparison methodology adopted here is presented in
Figure 4.1. The main elements of Figure 4.1 and the methodology are described in
section 4.2 to 4.5. A spreadsheet model is presented in section 4.6.
Figure 4. 1 Overview of model development methodology
Task 2
Task 1
Determination of Rail Fuel Efficiency
Selecting fuel efficiency measuring unit
Classifying commodities
Defining route characteristics
Determining train characteristics
Establishing a relationship between those parameters and
fuel consumption
Determination of Road Fuel Efficiency
Defining route characteristics
Determining road vehicle characteristics
Fuel consumption model selection and
Task 3 Calibration, validation and application of the model.
Fixing of the sub model
parameters
Harmonization of the model using vehicle
simulator
Application of the two sub-models to a comparison model
46
4.2 General model requirements
4.2.1 Selecting the fuel efficiency measuring unit
MJ per tonne-km is adopted as the fuel efficiency measuring unit. The advantage of
using MJ per tonne-km is the ability of the unit to measure the freight task as well as
distance moved. A similar unit of fuel efficiency measurement has also been used by
past comparative studies ranging from ATC (1991) to Affleck (2002).
The summary table of the comparison model compares the different freight task
options based on MJ per tonne-km.
4.2.2 Classifying the commodities
In some cases quantifying the energy used in terms of MJ per tonne-km would not
totally describe other various aspects of freight task (BT, 1995). For example,
moving one tonne of cotton or steel would be different because of the density
variation between cotton and steel, in which the former would require larger space to
move. Hence commodities are classified for better understanding of freight
movement and the energy consumption related to them. The adopted classification is
shown in Appendix A.
4.2.3 Route characteristics
Routes sections (for both rail and road movements) are to be divided based on
homogeneity of alignment characteristics including grade and curvature, as far as
possible. In addition, for road transport, homogeneous route division based on
congestion and pavement roughness assist in better estimating the fuel consumption.
For rail corridor descriptions, Houghton and McRobert (1998) used parameters such
as track length; number; location and length of crossing loops; level crossings;
number and type of sleepers (timber, concrete and steel); rail gauge (standard, narrow
and board); height clearances; ballast (type and depth); track alignment; average
speed and speed limits; signalling systems; and axle load limits. EC (1999) and
Jorgensen and Sorenson (1998) suggested the use of number of stops and speed to
47
describe the train operating characteristics. Based on these recommendations and
much used approach of Davis Formula (AREMA 1990), the route and operating
condition description included parameters such as grade, curvature, track gauge,
length of train, speed and mass.
4.2.4 Determining vehicle characteristics
Road
Since it is not possible to model each individual vehicle in the traffic stream, we resort
to the use of ‘representative vehicle’ for calculating energy consumption.
In this thesis, the vehicles are classified in the following broad categories, namely:
• Light commercial vehicle (includes utility and two axle single tyred truck)
• Heavy commercial vehicle
o Rigid
o Articulated
These categories are further divided into total of 19 different vehicle classes used in
the model. The characteristics of the vehicles and respective grouping are shown in
Appendix B.
Rail
Similarly for rail, fuel consumption depends on power and size of train. The trains are
not classified in this study. The approach used to determine the power of the train
depends on forces that the train overcome during the propagation. The assumption is
the locomotive (or the combination of locomotives) to the nearest match of the power
demand is available to drag the train on the track.
4.2.5 Data collection
The train consist information of various types of train in operation between Brisbane
to Toowoomba was collected from Queensland Rail (courtesy: Mr. Mark Nash). For
the rail track information between Helidon to Gowrie, reports published by QR have
been referred (QR 2001 and QR 2003). These are discussed in Chapter VI.
48
The route profile data of the Warrego Highway between Postman Ridge and Gowrie
Junction was collected from Toowoomba District, Department of Main Roads
(courtesy: Mr. Doug Head). The data was extracted from the provided drawings. A
separate speed profile drawings were provided so as to aid in estimating speed of the
assumed vehicle run.
Brunswick Street Office of Queensland Transport (courtesy: Mr. Les Brusza) assisted
while calibrating the road sub-model with the help of vehicle run simulator, namely
Design Pro. The Design Pro is a vehicle run simulator proprietary to Caterpillar Inc.,
Peoria, Illinois, USA. They advocate that Design Pro would best specify the Cat
engine and the best drive-train for any application with manufacturer specific product
information.
4.3 Road transport sub-model
4.3.1 Background
The criteria adopted here in selecting a road transport sub-model were as follows:
• Ease of use
• Availability of appropriate input data
• Applicability of the model to Australian conditions
• Ability to deal adequately with the different energy influencing parameters
NIMPAC style models satisfy three of those four selection criteria. NIMPAC is easy
to use and understand due the simplicity of its algorithm. Input data set contain
parameters that are easily available on the public domain and that are simple to
understand and input. NIMPAC style model has been widely used in Australia. The
Queensland Department of Main Roads is also using NIMPAC models for estimating
Vehicle Operating Cost (VOC) parameters for non urban road project evaluation.
NIMPAC style model being discussed here uses Look-Up Table approach. Thoresen
(2003) reported that the look-up table approach does not allow the analyst to
compute the combined, direct and indirect, effects that a particular traffic parameter
may have on fuel use. For example, an increase in average gradient will directly
increase fuel consumption by a specified amount at every speed of travel, but may
49
also indirectly affect fuel use through a reduction in the estimated speed of travel,
could only estimate the fuel consumption by an appropriate choice by the user of
travel speeds.
NIMPAC was analysed in terms of dealing adequately with fuel estimation. The
general algorithm of the NIMPAC model is (Thoresen and Roper, 1996):
+++++
×=
Adjustment
Congestion
Traffic
Adjustment
Roughness
Road
Adjustment
Curvature
Adjustment
Gradient
Adjustment
Efficiency
Engine
1
ipRelationsh
Fuel/Speed
Basic
00km)(litres/10
nConsumptio
Fuel
Eq. 4.1
Basic Fuel Speed Relationship
The literature review revealed that speed is one of the main parameters governing fuel
consumption. The basic fuel/speed consumption relationship is adopted from
NIMPAC model, which is: (Thoresen and Roper, 1996)
Basic fuel consumption (l / 1000 km) = A + B / speed + C * speed2 Eq. 4.2
This basic fuel speed relationship predicts the amount of fuel consumed over a flat
straight road assuming vehicles at approximately constant speed with a complete
absence of traffic congestion. Hence, the basic relationship can only shows how fuel
consumption varies with various constant speed of vehicle.
The coefficients of Eq. 4.2 vary with vehicle class and types. Appendix B contains the
data presented by Thoresen (2003) for those constants. The value of coefficients (A
and B) increase as the vehicle gets heavier. The value of coefficient “C” remains
almost constant, between 0.015 and 0.02, for vehicles considered in this study.
Figure 4.2 shows the variation in fuel consumption for different speed and vehicle
when they are drawn for vehicle travelling on flat and straight road section, NRM
roughness count of 100/km and a volume to capacity ratio of 0.5. Hence, Figure 4.2
does not represent basic fuel demand based on basic speed/fuel relationship; however
it shows the cumulative impact of factors mentioned above. The figure shows that the
effect of speed on fuel consumption would be more prominent as the vehicle gets
heavier. It portrays that the fuel efficient range comes in between 50km/h to 70km/h
depending upon the type of vehicle.
50
Fuel cosumption VS Speed
0
500
1000
1500
2000
2500
3000
0 20 40 60 80 100 120
Speed
Spe
cific
Fue
l Con
sumption
(litres
per 100
0 km
)
Utility Vehicle
Large Rigid Truck (3 axle 10 tyres)
Articulated Truck (4 ax le)
B Double
Figure 4. 2 Fuel consumption versus vehicle speed Source: NIMPAC Model
It was found that one of the important missing parameters on fuel consumption
subroutine of NIMPAC style model is payload. This thesis is concentrated on freight
movement comparison and vehicle payload is of prime importance.
Vehicle operating modes
The operation of the vehicles is to be divided into homogenous sections, according to
vehicle and route characteristics. Each section should be differentiated with every
change of speed and payload. And for even finer estimation, it is recommended to
differentiate the section with change in road roughness, curvature, gradient and
congestion level.
Vehicle types
Vehicle type is to be chosen from the set of representative vehicles. The
representative vehicle set is adopted from Thoresen (2003). If any new set of vehicle
is to be entered then the base data should be increased with the specific fuel
consumption data (or speed and specific fuel consumption relation), along with the
required set of data for correction factors such as payload, road roughness, congestion
level and gradient.
51
4.3.2 Amendment to NIMPAC algorithm
As mentioned in section 3.2.2, payload has a significant impact on energy
consumption. Ghojel and Watson (1995) reported a good linear fit between basic fuel
consumption and payload. IFEU and SGKV (2002) also successfully used a
multiplying factor to incorporate the effect of variation in payload. It is already a
tested approach to quantify a variation in payload as a multiplying factor. Therefore a
payload correction factor was applied in Eq. 4.1, which becomes:
+++++
×=
Adjustment
Congestion
Traffic
Adjustment
Roughness
Road
Adjustment
Curvature
Adjustment
Gradient
Adjustment
Efficiency
Engine
Factor
Correction
Payload
1
ipRelationsh
Fuel/Speed
Basic
00km)(litres/10
nConsumptio
Fuel
Eq. 4.3
Payload correction factor
ARFCOM (and HDM-4) model also conferred a high importance to the vehicle mass
in fuel estimation. The concept of load factor is an appropriate method for the
adjustment of fuel consumption rates, which is a function of vehicle mass as well.
CSIRO, PPK and UniSA (2002) suggested a linear relationship between load factor
and correction factor for fuel consumption as shown in Figure 4.3. Here the load
factor implies the ratio of the load a vehicle is carrying to the total load that vehicle
can carry and load correction factor implies the corresponding correction factor
(multiplying) to be entailed in fuel estimation equation.
Relationship between load factor and correction factor for
fuel consumption
0.8
0.85
0.9
0.95
1
1.05
1.1
1.15
1.2
0 10 20 30 40 50 60 70 80 90 100
Load factor (%)
Load C
orr
ection F
acto
r
Figure 4. 3 Relationship between load and fuel consumption correction factor Source: CSIRO, PPK and UniSA (2002)
52
Figure 4.3 shows that load correction factor increases linearly with the increase in the
load factor. Load correction factor (LCF) is 1 for 50% indicating the establishment of
the basic fuel consumption relationship for 50% load factor. CSIRO, PPK and UniSA
(2002) suggested fuel estimation equation is:
]**)(*
)/(*Pr*[)(
LengthijLCFijSCFFactorCorrectionSpeed
ijkmLnconsumptioFuel
itypeVehicle
jtypeFuelijoportionFleetVolume
LnConsumptio
FuelLink∑=
In the model developed here, the mass that the vehicle is carrying is input by the user.
This would induce a correction factor to incorporate the effect of mass on fuel
performance of the vehicle. Payload correction factor was derived by running the
computer based vehicle simulation model namely Design Pro.
4.3.3 Adjustment factors
Engine efficiency Adjustment
State of tune factor (FCAVF) models the engine efficiency adjustment factor of Eq.
4.1. The state of tune factor is dependent on type of vehicle. Thoresen (2003)
expanded the limited vehicle categories of the NIMPAC model by including more
combination of rigid truck and articulated vehicles. The extended vehicle set was
accompanied by the corresponding FCAVF value.
Thoresen (1988) reported that on ninety vehicles tested, the tuning of vehicles to
manufacturers’ specifications had minimal effect on fuel use. On average, data
indicated that untuned vehicles consumed only about one per cent more fuel
compared with their fuel use when tuned.
Gradient Adjustment
NIMPAC style models use two separate paths to quantify the effect of grade - one is
direct and the other is indirect via its effect on speed (Thoresen and Roper 1996).
Since speed is not estimated internally in this model, the second effect has not been
considered.
53
The input grade along with speed data and vehicle type will determine the gradient
adjustment term of Eq. 4.1. The model uses the revised NIMPAC gradient adjustment
lookup table presented in Thoresen (2003). The gradient adjustment was revised to
incorporate the effect of grade ranging from 4% to 10% for the extended set of
representative vehicles. Appendix C gives an overview of the gradient adjustment
lookup table.
Fuel cosumption VS Grade
0
500
1000
1500
2000
2500
0 2 4 6 8 10 12Grade
Specific Fuel C
onsumption
(litres per 1000 km
)
Utility Vehicle
Large Rigid Truck (3
ax le 10 tyres)Articulated Truck (4
ax le)B Double
Figure 4. 4 Fuel consumption versus grade
Figure 4.4 shows the effect of grade on fuel consumption for vehicles travelling on
straight road section at 35km/h, NRM of 100counts/km and a volume to capacity
ratio of 0.5, based on the output of NIMPAC style model. Figure 4.4 shows that the
effect of grade in fuel consumption increases with increasing grade and the effect is
higher for heavy vehicles. This is discreet as one of the forces to overcome along the
motion line would be the product of grade (sine of the angle) and mass of the vehicle.
Hence rise in either of them would result in more fuel consumption. The slope of the
line representing light vehicles is expected to be less as the product of those factors is
less for such vehicles.
54
Curvature Adjustment
Horizontal curvatures are classified as per the design speed (km/hr) which would
resemble curves such as very curvy, curvy, less curvy and almost straight.
Horizontal curvature category
Very Curvy Curvy Less curvy Almost straight Corresponding design speed (km/h) 30 50 65 80
Corresponding radius range (metres) (Approx.) 60 85 155 255
Vehicle Category
Corresponding correction factors L.C.V. and Rigid trucks 0.1 0.2 0.2 0.1 Combination Vehicles 0.1 0.2 0.1 0.1 Table 4. 1 Horizontal curvature adjustment factor
The user needs to select curvature from the listed group. In order to apply these
factors, details of the proportions of total road length applying to each of the four
curve categories are required, with the final curvature correction factor being
calculated in terms of the weighted average. As an alternative, road sections are to be
categorized homogeneously according to the curvature type so as to induce a
predetermined curvature correction as mentioned in Table 4.1.
The effect of curvature via speed on fuel performance of vehicle has not been dealt in
this model.
Congestion adjustment
In addition to reducing average vehicle speeds, congestion also results also in
increased speed variation and associated acceleration and deceleration patterns.
These variations from the steady speed driving pattern, if pronounced, may result in
significant additional fuel use. Thoresen (2003) reported congestion impacts on fuel
use can be direct, in terms of adjusting the basic fuel use relationship for congestion
effects, and indirect, through congestion effects on speed. The congestion effect on
speed is not discussed here and is open for user input.
In the NIMPAC style model, the congestion impacts on fuel use is estimated using an
adjustment factor obtained by multiplying the Volume to Capacity Ratio (VCR) by a
55
variable, FCONG, which adjusts fuel use to a level associated with a VCR of unity.
This is the maximum value applicable, and there is no further adjustment when
values of VCR higher than unity are applicable. Values of FCONG applicable to
individual vehicle types are shown in Table 4.2 which has been adopted from the
NIMPAC model.
Table 4. 2 Traffic Congestion Adjustments to Fuel Consumption (FCONG) Source: Thoresen (2003) Congestion adjustment = MIN (1, VCR) * FCONG The maximum possible congestion adjustment based on VCR is the FCONG value for the vehicle type. Figure 4.5 shows the graphical representation of variation of fuel consumption with
respect to congestion based on the output of NIMPAC style model. The graph is
drawn for straight and flat road section with NRM of 100counts/km and travel speed
of 60km/h.
Fuel cosumption VS Congestion
0
200
400
600
800
1000
1200
1400
0 0.5 1 1.5VCR
Specific Fuel Consumption
(litres per 1
000
km)
Light Vehicles
Heavy Vehicle
Figure 4. 5 Fuel consumption versus congestion
The effect of difference in traffic congestion adjustment factor for heavy and light
vehicle is depicted in Figure 4.5 by the difference in the slope of two lines. Figure 4.5
shows the linear variation of fuel consumption with volume to congestion ratio (VCR)
till VCR reaches 1. As expected, VCR of 1 or above results in long queue spillbacks
56
at intersections and delays along the route. The impact of VCR above 1 on fuel
consumption could well be reflected by VCR of 1, as both represent a very congested
condition. Greater impact of congestion on heavy vehicle could be the representation
of more idling and stop/start fuel demand for heavy vehicle compared to light and also
the high fuel demand at low speed for heavy vehicle compared to light.
Roughness Adjustment
Road roughness has been divided into five levels ranging from very good to very
poor. The division is based on Thoresen and Roper (1996).
Table 4. 3 Classification of road section based on roughness Source: Thoresen and Roper (1996)
NIMPAC models calculate a pavement condition cost factor, GCGFAC, which is
combined with another factor, FCGRVF, in order to derive appropriate roughness fuel
consumption adjustment factors. GCGFAC values are common to all vehicle
categories, whereas FCGRVF is sensitive to speed and vehicle category.
GCFGAC =
−− )/()(* PAVCNRMAPAVCCNRMCSENSP
CFSMAXMinimum Eq. 4.4
where:
GCGFAC = Pavement condition cost factor
CFSMAX = Maximum cost factor (fuel and tyres) for surfaced roads
CSENSP = Cost sensitivity for surfaced roads
NRMA = Coefficient of the PSR to NRM conversion ratio
PAVC = Minimum roughness of road section after
construction/reconstruction
CNRM = Current road roughness in NRM counts per kilometre
As variations in the values of model variables in the above equations can cause
differences in fuel roughness adjustments between models, Thoresen and Roper
(1996) recommended that these be set as follows in order to harmonise resulting
estimates: Values for CFSMAX and CSENSP should be set at 1.75, PAVC be
57
assigned a value of 50 (NRM counts per km), and NRMA should be assigned a value
of 250 (NRM counts per km).
Thoresen (2003) presented a revised lookup table for FCGRVF. Appendix D gives an
overview of FCGRVF lookup table which is used here to estimate the roughness
impact on fuel consumption.
The final roughness correction factor is then the product of FCGRVF and GCGFAC,
(Thoresen and Roper, 1996) which makes roughness adjustment a function of vehicle,
speed and road surface parameters.
Fuel cosumption VS Road Roughness
0
200
400
600
800
1000
1200
1400
0 50 100 150 200 250 300
NRM Counts/km
Spe
cific
Fue
l Con
sumption
(litres
per 100
0 km
)
Utility Vehicle
Large Rigid Truck (3
ax le 10 ty res)Articulated Truck (4
ax le)B Double
Figure 4. 6 Fuel consumption versus road roughness
Figure 4.6 portrays the variation in specific fuel consumption as per NRM counts/km
for different vehicles travelling at 65km/h and 0.5 volumes to capacity congestion on
a straight and flat road section based on output of NIMPAC style model. Sensitivity
of road roughness on fuel consumption varies with vehicle type. The effect of
variation in NRM counts per km on specific fuel consumption is greater for heavier
vehicles, as shown in Figure 4.6. This difference is prudent since the balancing
vertical component of the forces would be the function of roughness coefficient
(which is denoted by NRM counts here) and mass of the vehicle. Hence the greater
the mass of the vehicle/roughness value, the higher would be the energy required to
overcome the friction due to roughness.
58
4.3.4 Summary for road
Table 4.4 shows the minimum and maximum effect of each adjustment factor. Other
circumstance, such as use of the parameter for the portion of total run also play an
important role in total energy estimation, along with the absolute maximum and
minimum mentioned in Table 4.4.
Adjustment Factor Affected by Min. effect Max. effect
Engine efficiency Type of vehicle 7% 10%
Gradient Grade, speed and vehicle type 0% 123%
Curvature Curve and vehicle type 0% 20%
Congestion VCR and vehicle type 0% 40%
Roughness Road surface, speed and vehicle type 0% 48%* *48% for poor road surface (NRM/km = 250), affect of the factor would rise as NRM counts increase.
Table 4. 4 Adjustment factors
4.3.5 Vehicle simulator
The Design Pro software (refer section 4.2.5) was used to determine the effect of
payload on vehicle energy consumption. Several simulation runs were performed.
Figure 4.7 portrays the results in graphical form for a typical B-Double simulated
run.
Fuel consumption vs Gross Vehicle Mass (GVM)
0
50
100
150
200
250
300
350
400
450
0 10000 20000 30000 40000 50000
GVM
lt/1
00
0k
m
72 kmph
80kmph
89kmph
97kmph
105kmph
Figure 4. 7 Effect of Gross Vehicle Mass in Energy consumption
59
A linear increase in the energy consumption with the increase in Gross Vehicle Mass
(GVM) was obtained. This could be explained with linear increment in inertial energy
demand to propel the vehicle with the increase in mass of an object. The slope of the
lines in Figure 4.7 is almost constant (approx. 0.004 or 0.230).
Payload term could be used more effectively than GVM, especially in case where
amount of freight being moved is of prime importance. Assuming a constant tare
weight of a typical B-Double as 19.5 ton and total GVM capacity as 53 ton, the above
relationship could be changed in terms of payload and energy consumption.
Payload vs Fuel consumption (B Double)
0
50
100
150
200
250
300
350
400
450
500
0 0.2 0.4 0.6 0.8 1
Payload
Fu
el
co
nsu
mp
tio
n
(lt/
1000 k
m)
72 kmph 80 kmph
89 kmph 97 kmph
105 kmph
Figure 4. 8 Relationships between payload and energy consumption The linear curves fitting in above points would give the correlation coefficients of
more than 0.9 and the relationships of the form:
CPayloadkmltnConsumptioFuel +×≈ 210)1000/(
The high slope of the lines (around 210) indicates that fuel consumption is very
sensitive to payload factor. Hence inclusion of payload term, in NIMPAC style
model, to fit the purpose of energy quantification is essential.
_____________________________________________________________________________________ * Design Pro is a vehicle run simulator proprietary to Caterpillar Inc., Peoria, Illinois, USA. Design Pro is expected to best specify the Cat engine and the best drivetrain for any application with manufacturer specific product information.
60
The variation in the speed did not show a high fluctuation in slope of the lines.
However, as expected the lines are shifted up for every increase in the speed
corresponding to the higher energy demand for higher speed.
4.4 Rail transport sub-model
Due to the unavailability of rail fuel consumption data and high degree of uncertainty
involved in the use of average MJ/NTK and MJ/GTK values, the rail transport sub-
model is developed based on the existing practices reviewed in the literature and
personal communication with Dr. Peter Pudney and Prof. Phil Laird.
Rail energy consumption can be estimated based on the equation of motion taking the
train a point mass moving along a smooth track under the influence of an applied
force:
)()(),(/ xTvRuvFdtdvm +−=×
Where: m is the mass of the train; v is the speed of the train; F is the tractive force
produced at the wheels; u is the control setting; R(v) is the resistive force acting on
the train; and T is track force, due to gradient and curvature, acting on the train and x
is the location of the train.
Because of the inertia of rotating parts, the effective mass of the train is slightly
greater than the actual mass. The difference between actual mass and effective mass
is small, particularly for long-haul trains, and can be ignored.
Tractive force
The tractive force required to maintain a constant speed ‘v’ is:
F = R(v) - T(x).
The associated tractive power at the wheels is:
P = v [R(v) - T(x)] ----- ----- ----- Eq. 4.5
Resistive force
Resistance acceleration is usually modelled as a quadratic function of speed.
)()()( 2210 vrvrrvR ×+×+= ----- ----- ----- Eq. 4.6
61
The coefficients r0, r1 and r2 are particularly difficult to estimate, but will generally
increase with the length and mass of the train. AREMA Manual for Railway
Engineering has tabulated predominate but not exclusive contributors to the
coefficients (r0, r1 and r2).
Source: AREMA (1990)
Table 4. 5 Coefficient contributors
The following formulae are based on work done by Lukaszewicz (2001):
)08.0(5
)58.0(22
)000009.0()65(
2
1
0
traintheofLengthr
traintheofLengthr
traintheofMassaxlesofNumberr
×+=
×+−=
×+×=
Eq. 4.7
The coefficients were derived for ordinary freight trains of mixed consist on a
straight rail track in Sweden.
Track force
The force due to the track can be modelled as
)()()( xCxGxT −= Eq. 4.8
where G is the gradient force acting on the train, and C is the force acting against the
train due to the curvature of the track.
Gradient force is positive on declines and is given by:
))(()( xSingmxG θ××= Eq. 4.9
where θ is the angle of slope of the track and g is the acceleration due to gravity
(9.8m/sec2).
The curvature force is usually assumed to be independent of speed. Resistance due to
curvature has been widely used as 0.8 lb/ton per degree of curvature (AREMA
62
1990), where degree of curvature is the change in bearing on a curve with a 100 foot
chord. For other than standard gauge track, the following relationship was proposed:
Rc = 0.17 × Gauge in feet
where Rc is the curve resistance in lb/ton per degree of curvature.
The width of the narrow, standard and board gauges are shown in Figure 4.9 which
would enhance the understanding of the proposed curvature penalty.
Figure 4. 9 Gauge width dimension
Taking the curvature penalty as 0.8 lb/ton for a degree of curvature for a standard
gauge track and using SI units, the force acting against the train on a curve with
radius r(x) is
)(
33.6)(
xr
massxC
×=
The ratio proposed in AREMA (1990) was used for determining the curvature penalty
for various gauge width track. Hence curvature penalty would be;
)()(
33.6)( tracktheofwidththeonbasedRatio
xr
massxC ×
×= Eq. 4.10
where the ratio would be 1 if the track is standard gauge (4 feet 8.5 inch) and 1.11 if
the track is board gauge (5 feet 3 inch).
In addition, AREMA (1990) recommended a proportional reduction in curve
compensation in presence of wayside rail lubrication and/or improved wagons and
track.
63
Combination of equations from Eq. 4.5 to Eq. 4.10 gives the power required to
maintain a constant speed ‘v’ on a track with constant gradient and curvature.
Fuel flow rate
Taking into account the efficiency of the traction system and the fuel consumption of
the diesel generator, the rate of fuel consumption can also be estimated based on
Power calculated from the combination of Eq. 4.5 to Eq. 4.10.
The Specific Fuel Consumption (SFC) of diesel engine plays an important role in
predicting the amount of fuel being used to generate the required energy. SFC is
dependent on the engine design and particularly sensitive to compression ratio. Thus,
any change in specific fuel consumption of diesel generator would impact the fuel
consumption estimation. The developed spreadsheet tool allows the users to
overwrite the default value.
However, the difference in the SFC between different engines tends to be quite
small. Specific fuel consumption of diesel generator at full power when installed in
locomotive was taken as 0.23kg/kWh; converting to SI units gives 6.4 × 10-8 kg/J.
The specific gravity of diesel fuel is 0.83, and so the volumetric fuel consumption is
7.7 × 10-8 litres/J, and the fuel flow rate will be 7.7 × 10-8 (litres/s)/W.
When the power required in maintaining a constant speed is P (in watts), the
corresponding fuel flow rate will be:
Fuel Demand = 7.7×10-8 ×P × Duration / η Eq. 4.11
where η is the efficiency of the electric traction system which vary depending on the
engine used and the track speed of the locomotive. According to AREMA (1990), the
efficiency of diesel-electric locomotives would be in between 80% to 85%.
64
Idling power of the locomotive
Lukaszewicz (2001) provided an empirical mean value (66 kW) originating from
idling and coasting of freight trains. Converting the values to SI units, it would be
66000 Joules/Sec which is the value adopted in this study.
Braking and Accelerating energy
Braking is relatively difficult to model due to the uncertainty in type of brake used. In
particular, mechanical braking is used to supplement dynamic (electrical) braking at
low speed (Howlett and Pudney 1995).
In the model, the rate of fuel supplied was assumed to be zero during braking.
However, in practice a low notch setting is often used to operate the electrical brakes
(Howlett and Pudney 1995). However, the precise nature of braking was not
considered in overall fuel estimation.
The braking at any stage of the journey might necessitate excessive application of
power at some other stage to accelerate the vehicle. The combination of equations
(Eq. 4.5 to Eq 4.10) would give the energy needed to run the train in a constant
speed. However, additional energy is required for a train to accelerate.
The tractive effort needed in each step to overcome resistance and acceleration can
be estimated as described in Eq. 4.12. The latter is based on Rochard and Schmid
(2000) and the assumption that coefficient for rotating masses (including wheels,
shafts and axles) is almost equal to unity and can be ignored, particularly for long-
haul trains.
ResistancemassonacceleratiEffortTractive += * Eq. 4.12
The power required at each step would be the product of tractive effort and speed at
that step. Based on this power, the fuel demand for accelerating could be estimated
using Eq. 4.11 and Eq. 4.12.
65
However, this process requires the power (resulting to fuel flow rate) estimation for
every instant. The iterative work involved was excluded in this study by considering
the accelerating section as the speed holding section with average speed.
The change in energy demand due to differing type of train movement is explained
below. For instance, if the train of 2864 tonnes is to come to 60km/hr speed from rest
in 10 minutes (acceleration 0.028 m/sec2; distance 5.04 km), the fuel demand would
be 31.1 litres (based on Eq. 4.12 and assuming the efficiency to be 1). However, if
the same movement is assumed to be under constant average speed of 30km/h then
the model would give the result to be 15.5 litres. Hence the effect of change in speed
is prominent and highlights the importance of driver behaviour.
In the case study (Chapter VI), the fluctuation in the speed has not been taken into
consideration because of the high degree of uncertainty in the speed profile of the
considered options. Since in all the options (including road), the energy demand for
the change in the velocity is not considered, the result of the comparative study is not
expected to alter by a significant amount. In addition, when the section is significantly
long, the energy required to accelerate the train would only made up a small section of
the total energy demand. Hence in such cases, which are what the tool is directed for,
a prudent result could be expected.
4.5 Additional transport process sub-model
4.5.1 Intermodal transfer energy
The amount of energy required to transfer freight from one mode to another is
grouped in the energy demand of intermodal transfer. This energy demand depends
upon various factors such as:
• Intermodal transfer platform area;
• Handling equipments in use;
• Mass of the freight;
• Size and number of containers; and
• Management.
66
Andersen et al (2001) reported the energy efficiency of goods handling in a transfer
station. The energy efficiency reflects the data gathered from six port operators
grouped by different loading ways. Table 4.6 shows the values which are used to
estimate the intermodal transfer energy.
Type Energy Efficiency (kwh/tonne) Energy Efficiency (MJ/tonne) Bulk 0.9 3.24 Average 3.7 13.32 Source: Andersen et al (2001)
Table 4. 6 Intermodal transfer energy
4.5.2 Shunting energy
Shunting process also requires additional energy. Shunting is mainly carried out
using diesel locomotives. Typical energy values for shunting that IFEU and SGKV
(2002) used is 0.03 kg diesel fuel per gross tonne. In literature, a considerable
difference in the typical shunting values could be found. For instance, Andersen et al
(2001) found that two diesel locomotives (operated in two shifts, 16 hrs/day/engine)
would use 0.35 litres fuel per net tonne as a shunting energy demand.
IFEU and SGKV (2002) recommended that the significance of shunting energy
demand is less while analysing the corridor level energy gain. Hence, even with the
considerable variation in the reported shunting energy, an arbitrary value proposed
by IFEU and SGKV (2002) is considered in this study with the conversion factor of
38.6 MJ/litre and specific gravity of 0.83. In case of access to more reliable value by
the user, the tool allows the user to replace the default value.
Energy Efficiency kg/ gross tonnes lt/ gross tonnes MJ/ gross tonnes
Shunting processes 0.03 0.036 1.39
Table 4. 7 Shunting energy demand
4.6 Spreadsheet model platform
Section 4.2 to 4.5 discussed the development three sub-models needed for a
comprehensive analysis of energy advantage of various modes and options involved.
For ease in use of such models especially with the combination, a spreadsheet tool
67
was developed. This section briefly describes the three distinct sections of the
spreadsheet tool namely input, computation and output. Appendix E contains more
elaborative description and discusses how to operate the tool. Appendix F contains a
CD which has the spreadsheet tool developed as a part of this study.
The spreadsheet has nine sheets namely: input freight characteristics, input road,
input rail, vehicle characteristics, lookup tables, calculation, output road, output rail
and summary table. The interrelationships between the sheets is summarised in
Figure 4.10.
Figure 4. 10 Flow diagram of the comparison spreadsheet tool
The Input Freight Characteristics sheet allows the user to define, and later identify,
the freight characteristics such as type of freight, size of freight and type of
commodity. In some cases quantifying the energy used in terms of MJ per tonne-km
would not totally describe other various aspects of freight task (BT 1995). The major
deficiency of the measurement is the inability to deal with the volume of the task,
which would govern the number of containers and trips ultimately affecting the final
energy consumption. These parameters may be tallied at first so the user is better
Input Sheet
Freight characteristics
Input road
Input rail
Lookup tables
Estimating energy required for road movement section including pick up and delivery
Estimating energy required for rail movement section including road pick up and delivery
Output sheet
Road Rail
Summary sheet (Comparison)
Helps in identifying the freight task Informs users about the size of containers and number of trips required
68
informed about the number of containers required to carry the commodity and trips
generated for the task. The main aim of this sheet is to make an allowance for such
judgement by informing users about the available volume and freight volume.
The Input Road sheet allows user to input the freight movement characteristics of the
pickup, road line haul and delivery section. The sheet contains space to input 15
pickup and delivery legs at once. Each pickup/delivery leg description has 5 rows.
Each row allows segregation based on traffic and terrain characteristics of freight
task. Road line haul section has three segments with fifteen rows in each segment.
Each of those rows allows segregation based on traffic and terrain characteristics of
freight. Three segments separated here allow three different vehicles of the same
freight fleet to be considered at once for energy consumption comparison. Repeated
run of the spreadsheet tool is necessary to encompass the energy performance of
more number of vehicles on the fleet (more than three, if any) at once.
Figure 4. 11 Input rail sheet
69
Similarly the input rail sheet provides the user to input the freight movement
characteristics involving road for pickup and delivery, and rail for line haul
movement. The screenshot of Input Road Sheet is shown in Figure 4.11.
Lookup table and calculation sheets quantify the adjustment factors based on
tabulated values and formulae based on section 4.2 to 4.5..
The output sheets present the result after the computation. The road and rail output
sheets present the energy demand for travelling each segment of road or/and rail and
for each activity. Figure 4.12 shows a sample ‘Output (Road)’ sheet. The summary
table sheet compares the energy required for pickup, line haul and delivery legs for
options mentioned on input road sheet and input rail sheet to depict the overall modal
freight energy. The screenshot of summary table is shown in Figure 4.13.
Figure 4. 12 Output Road Sheet
#VALUE!
0
Section Vehicle
Efficiency
Adjustment
Road
Length
(km)
Speed
(kmph) Payload
Payload
Factor
Congestion
(VCR)
Congestion
factor Grade (%)
Grade
Factor Curvature
Curvature
Factor
Roughness
(NRM/km)
Roughness
Factor
Fuel
consumption Start point End point
PU01 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU02 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU03 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU04 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU05 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU06 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU07 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU08 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU09 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU10 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU11 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU12 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU13 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU14 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU15 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU16 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU17 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU18 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU19 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU20 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU21 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU22 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU23 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU24 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU25 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU26 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU27 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU28 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU29 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU30 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU31 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU32 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU33 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU34 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU35 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU36 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU37 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU38 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU39 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU40 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU41 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU42 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU43 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU44 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU45 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU46 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU47 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU48 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU49 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU50 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU51 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU52 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU53 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU54 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU55 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU56 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU57 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU58 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU59 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU60 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU61 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU62 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU63 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU64 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU65 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU66 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0PU67 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU68 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU69 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU70 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU71 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU72 B Double 1.1 0 0 0 0 0 5 0.05 0 0 0 0 0 0PU73 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU74 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
PU75 B Double 1.1 0 0 0 0 0 0 0 0 0 0 0 0 0
0
Section Vehicle
Efficiency
Adjustment
Road Length
(km)
Speed
(kmph) Payload
Payload
Factor
Congestion
(VCR)
Congestion
factor Grade (%)
Grade
Factor Curvature
Curvature
Factor
Roughness
(NRM/km)
Roughness
Factor
Fuel
consumption Start point End point
RoLH01 B Double 1.1 0.122 70 0 0.3 0.3 1.45 0.155875 4 0.1 100 0.0875 0 0
RoLH02 B Double 1.1 0.274 70 0 0.3 0.3 0 0 4 0.1 100 0.0875 0 0
RoLH03 B Double 1.1 0.166 60 0 0.3 0.3 1.56 0.1677 4 0.1 100 0.0853125 0 0RoLH04 B Double 1.1 0.34 60 0 0.3 0.3 1.56 0.1677 0 0 100 0.0853125 0 0
RoLH05 B Double 1.1 0.347 60 0 0.3 0.3 1.56 0.1677 4 0.1 100 0.0853125 0 0
RoLH06 B Double 1.1 0.183 70 0 0.3 0.3 0.32 0.0344 4 0.1 100 0.0875 0 0
RoLH07 B Double 1.1 0.071 70 0 0.3 0.3 0 0 4 0.1 100 0.0875 0 0RoLH08 B Double 1.1 0.294 75 0 0.3 0.3 0 0 0 0 100 0.09078125 0 0
RoLH09 B Double 1.1 0.061 75 0 0.3 0.3 0 0 0 0 100 0.09078125 0 0
RoLH10 B Double 1.1 0.103 65 0 0.3 0.3 1.38 0.14835 0 0 100 0.0875 0 0
RoLH11 B Double 1.1 0.202 60 0 0.3 0.3 3.22 0.34615 3 0.1 100 0.0853125 0 0
RoLH12 B Double 1.1 0.183 65 0 0.3 0.3 1.52 0.1634 3 0.1 100 0.0875 0 0
RoLH13 B Double 1.1 0.321 70 0 0.3 0.3 0.55 0.059125 3 0.1 100 0.0875 0 0
RoLH14 B Double 1.1 0.105 70 0 0.3 0.3 0.55 0.059125 0 0 100 0.0875 0 0
RoLH15 B Double 1.1 0.183 75 0 0.3 0.3 0 0 0 0 100 0.09078125 0 0
Operating Characteristics
Pick Up Section
Operating Characteristics
Road line haul section
Output Sheet (ROAD)Identification code Origin rahs Other freight - Unitised Type of
commodity
Chemical related products
not elsewhere specifiedDestination jaejrType of packing
Option code
This section is for one set
of vehicle in the fleet. However input does allow
the user to change the
type of vehicle as per the section in the case where the vehicle in the fleet are
stopped at some point and freight is loaded into
another vehicle.
Operating Characteristics
70
Figure 4. 13 Summary sheet
4.7 Summary
This chapter discussed the use of some existing models and some previous
recommended values to estimate the corridor level energy consumption. This chapter
also highlighted the development of a spreadsheet comparison tool. The chapter
proposed some amendments in the existing models to compare the vehicles and
corridor options based on energy performance. The proposed models, on which
there are some amendments to fit the requirements are:
• NIMPAC Style model
• Davis Formula updated by Lukaszewicz (2001)
For further enhancement in the confidence level of the model, it is recommended to
verify the models with on track testing techniques such as coasting down and
dynamometer testing.
71
CHAPTER V SENSITIVITY ANALYSIS
5.1 Introduction
Sensitivity testing of parameters can add greatly to the validity of an energy
estimation model. Here the parameter sensitivity tests are also used as validating tool
by confirming whether a small perturbation to a parameter’s numerical value results
in a significant change in the model’s behaviour. The results of these tests can
indicate the level of accuracy that is required when assigning numerical values to a
model’s parameters.
It can be impractical to run a sensitivity analysis for every possible value because of
the limitless possibilities to be simulated. A simple and straightforward process for
analysing the sensitivity of an energy consumption model is carried out in this
chapter. Sensitivity tests are performed on each model parameter discretely.
This chapter discusses the likely error ranges associated with the output of the
developed model when certain plausible assumptions are made about the
measurement errors of the various independent variables. The chapter also helps to
better understand the relationships between the parameters influencing energy
consumption and the relative importance of those parameters in energy estimation.
5.2 Model Errors
The search for models which more accurately represent complex situations and
interactions is worthwhile. However, it is not possible to model every complex
situation in a simple model. This deficiency of a model is evident through the output
error.
Richardson (2001) mentioned three types of errors associated with models. The first
type of error is the inability of the model to completely represent a given situation,
which is known as specification error. The second type is the error that arises through
poor input data which is known as measurement error. Hence measurement error is
the property of data and cannot be significantly reduced in the modelling process,
72
with the exception of model propagation (i.e. the means by which a model magnifies
or diminishes errors in different variables). The third type is sampling error which
indicates the extent to which results vary across different samples of same
population. The sampling error can be reduced by taking a larger sample.
The total output error of any model results from the combination of specification and
measurement errors. Intuitively the curve of specification error would slope
downward asymptotically with the increased complexity of model, whereas the
measurement error would increase with an increase in complexity of the model as
shown in Figure 5.1. Richardson (2001) mentioned that a more complex model will
reduce the specification error. However, it will also increase the chances of
measurement error. At some point, the inclusion of more variables into the model
will increase the measurement error more than it will reduce the specification error.
This trade-off between specification error and measurement error can be further
demonstrated by considering the use of a dataset which has a higher degree of
measurement error, as represented by e’meas curve in Figure 5.1. Under these
conditions, the measurement error will be higher at all levels of model complexity, as
will be the total error, as shown in Figure 5.1. The complexity is defined as being
measured by the number and structure of relevant explanatory variables included in
the model.
Figure 5. 1 Error versus Complexity
Source: Richardson (2001)
73
5.3 Errors and uncertainty in road energy estimation
5.3.1 Background
The road sub-model proposed in Chapter IV is tested for its sensitivity of adjustment
factors such as grade, roughness, payload, speed and curvature. The sensitivity of the
model estimation coefficients was scrutinized. Effect of changes in input values, such
as speed of 70 km/h instead of 75 km/h, was discussed in Chapter IV. This chapter
deals with the effect of change in value of the correction factor on energy estimation,
rather than the direct effect of alteration in input parameters such as change in speed,
roughness or grade. This chapter deals with the change in energy estimation for the
same speed (say 70 km/h) due to change in the estimation coefficients.
A simplified relationship between the energy influencing parameters is reinstated
below: (see Eq. 4.2)
( 1.5.1)/ 2Eq
Roughness
Congestion
Curvature
Grade
assuchFactors
Correction
factor
correction
Payload
vCvBAnConsumptioFuel
+×
××++=
As discussed previously, the remaining energy influencing parameters are fixed for a
sensitivity testing of single parameter. Table 5.1 shows the details of those values
and the parameters.
Parameters Sensitivity
Roughness (NRM/km)
Speed (km/h)
Grade (%)
Curvature
Congestion (Volume/capacity)
Roughness coefficients 100 100 Nil Nil 0.3
Speed coefficients 100 70 Nil Nil 0.3
Grade coefficients 100 70 and 35 2,4,8 Nil 0.3
Curvature coefficients 100 30 and 65 Nil 0.3
Congestion coefficients 100 70 Nil Nil 0.3 and 1
Payload Good Asphalt 72 to 113 Nil Nil Unknown
Table 5. 1 Constant values taken for sensitivity analysis of various parameters
The length of the road section is not expected to alter the sensitivity result
significantly. However, the length considered for the sensitivity analysis was 1000
km. For the consistency in testing, the same vehicle types were selected for each
sensitivity testing. The four different types of ‘representative vehicles’ selected are:
• B-Double
• Articulated 4 Axles Truck
74
• Rigid 3 Axles Truck
• Utility Truck
The details of the vehicles are given in Appendix B.
5.3.2 Roughness sensitivity
The overall sensitivity of roughness with fuel consumption was discussed in section
4.3.3. This section deals with the sensitivity of the coefficients of the roughness
correction factor (see Eq. 5.1).
The energy influencing parameters were fixed for the sensitivity testing of the
roughness parameter. Table 5.1 shows the constant values being used in the
sensitivity testing. Figure 5.2 shows the result of the roughness sensitivity analysis at
a NRM roughness count of 100 per km.
Roughness sensitivity
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20Alteration in Adjustment Factor (%)
Change in
Fuel
Consum
ptio
n (%
) B DoubleArticulated 4 axleRigid Truck 3 axlesUtility Vehicle
Figure 5. 2 Roughness sensitivity
A change of 20% in the roughness adjustment factor would bring a corresponding
change of about 0.7% in fuel consumption for B-Doubles and about 0.37% for Utility
vehicles. As expected, the effect increases for heavier vehicle and similarly with high
NRM value. The effect of alteration in roughness adjustment factor did not result in a
very significant change in fuel consumption.
5.3.3 Speed coefficients and speed sensitivity
This section deals with the sensitivity of the coefficients of basic speed fuel
relationships (Eq. 4.2). The alteration of all three speed coefficients simultaneously
by an equal amount would be reflected on energy consumption with the change in
same magnitude, hence showing a one to one relationship.
75
The energy influencing parameters were fixed for the sensitivity testing of the
roughness parameter. Table 5.1 shows the constant values being used in the
sensitivity testing. Figure 5.3 a, b and c show the result of the speed sensitivity
analysis of three different speed coefficients (A, B and C) mentioned in Eq. 5.1.
Speed sensitivity (constant term variation)
05
10
0 5 10 15 20
Alteration in Speed coefficient (%)
Change in f
uel
consum
ption
(%)
B-DoubleArticulated 4 axlesRigid 3 axlesUtility Vehicle
Figure 5. 3a Speed sensitivity (constant coefficient variation, A) Figure 5.3a and Figure 5.3b show that the effect of the alteration in constant
coefficient and reciprocal coefficient of the basic speed fuel relationship (first term,
Eq. 5.1) would have higher impact on energy consumption of heavier vehicles
compared to light. The exception to this is the Utility Vehicle while sensitivity
testing of two coefficients namely, A and C. The Utility vehicle was not showing a
consistent trend, which might be the effect of extreme lightness of the vehicle
compared to the remaining three.
Furthermore, the effect of constant and reciprocal coefficient alteration is quite
prominent on energy consumption which is represented by the line slope greater than
0.45.
76
Speed sensitivity (reciprocal term variation)
05
10
0 5 10 15 20
Alteration in Speed coefficient (%)
Change in fuel consum
ption
(%)
B-DoubleArticulated 4 axlesRigid 3 axlesUtility Vehicle
Figure 5.3b Speed sensitivity (reciprocal coefficient variation, B)
Speed sensitivity (square term variation)
05
10
0 5 10 15 20
Alteration in Speed coefficient (%)
Change in f
uel
consum
ption
(%)
B-DoubleArticulated 4 axlesRigid 3 axlesUtility Vehicle
Figure 5.3c Speed sensitivity (square coefficient variation, C)
Figure 5.3c shows that the effect of the alteration in square coefficient of the basic
speed fuel relationship (Eq. 5.1) would have higher impact on energy consumption of
lighter vehicles compared to heavy. C×v2 is expected to cover the resistance of
aerodynamic drag. Hence, the square coefficient (C) depends on aerodynamics of the
vehicle. Hence, it is prudent to assume that for a small vehicle change in
aerodynamics would have a higher impact on percentage of fuel used.
Same as other speed coefficients, the effect of square coefficient alteration is also
quite prominent on energy consumption which is represented by the line slope
between 0.17 and 0.37.
5.3.4 Grade sensitivity
77
The overall sensitivity of grade with fuel consumption was discussed in section 4.3.3.
This section deals with the sensitivity of the coefficients of the grade correction
factor (see Eq. 5.1).
The energy influencing parameters were fixed for the sensitivity testing of the grade
parameter. Table 5.1 shows the constant values being used in the sensitivity testing.
Figure 5.4 a, b and c show the result of the grade sensitivity analysis at 2%, 4% and
8% gradient. The figures portray that the grade sensitivity is higher for heavier
vehicles and higher grades.
Grade sensitivity at 2%
0
0.20.40.60.8
1
1.21.41.61.8
2
0 5 10 15 20
Alteration in adjustment factor (%)
Change in e
nerg
y
estim
ation (%
)
B-DoubleArticulated 4 axleRigid 3 axleUtility
Figure 5. 4a Grade sensitivity at 2% gradient
Grade sensitivity at 4%
0
0.5
1
1.5
2
2.5
3
3.5
0 5 10 15 20
Alteration in adjustment factor (%)
Ch
an
ge in
en
erg
y
estim
atio
n (%
)
B-DoubleArticulated 4 axleRigid 3 axleUtility
Figure 5.4b Grade sensitivity at 4% gradient
78
Grade sensitivity at 8%
0
1
2
3
4
5
6
7
0 5 10 15 20
Alteration in adjustment factor (%)
Change in e
nerg
y
estim
ation (%
)
B-DoubleArticulated 4 axleRigid 3 axleUtility
Figure 5.4c Grade sensitivity at 8% gradient
5.3.5 Curvature sensitivity
The overall sensitivity of curvature regarding fuel consumption was discussed in
section 4.3.3. This section deals with the sensitivity of the coefficients of the
curvature correction factor (see Eq. 5.1).
The energy influencing parameters were fixed for the sensitivity testing of the grade
parameter. Table 5.1 shows the constant values being used in the sensitivity testing.
Since the speed is a curvature dependent factor, the speed is varied for different
curvature sensitivity testing. Figure 5.5a shows the curvature sensitivity for very
curvy road where the limiting speed is 30 km/h and Figure 5.5b shows the curvature
sensitivity for less curvy road where the limiting speed is 65 km/h.
Horizontal curvature sensitivity (Very curvy section)
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20
Alteration in adjustment factor (%)
Change in
energ
y
cio
nsum
ptio
n (%
) Utility VehicleRigid Truck
Articulated TruckB Double
Figure 5. 5a Curvature sensitivity for very curvy section
79
Horizontal curvature sensitivity (Less curvy section)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 5 10 15 20
Alteration in adjustment factor (%)
Change in
energ
y
cionsu
mption (%
)
Utility VehicleRigid Truck
Articulated TruckB Double
Figure 5.5b Curvature sensitivity for less curvy section
The horizontal curve sensitivity rose as the degree of curvature decreases. This is
prudent as there is already a high degree of penalty for very curvy road so the small
change in fuel consumption would not make a huge difference in the energy
estimation. Moreover, as the curve is easier, the sensitivity of heavy and light vehicle
starts to separate whereas for relatively sharp curvature the degree of sensitivity for
heavy and light vehicles are almost same.
The curvature would make up only a small segment of total road being travelled in
most of the freight corridors. Hence during the comparison process of the energy
consumption, the effect of alteration in curvature correction factor is not expected to
make a huge difference.
80
5.3.6 Congestion sensitivity
The overall sensitivity of congestion with fuel consumption was discussed in section
4.3.3. This section deals with the sensitivity of the coefficients of the curvature
correction factor (see Eq. 5.1).
The energy influencing parameters were fixed for the sensitivity testing of the
congestion parameter. Table 5.1 shows the constant values being used in the
sensitivity testing.
Congestion sensitivity is carried out in the low congestion level and high congestion
level represented by Volume to Capacity Ratio (VCR) of 0.3 and 1 respectively.
Congestion sensitivity at 0.3 VCR
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20
Alteration in adjustment factor (%)
Change in e
nerg
y
estim
ation (%
)
B-DoubleArticulated 4 axleRigid 3 axleUtility
Figure 5. 6a Congestion sensitivity at light traffic section
Congestion sensitivity at 1 VCR
0
0.5
1
1.5
2
2.5
3
3.5
0 5 10 15 20
Alteration in adjustment factor (%)
Change in e
nerg
y
estim
ation (%
)
B-DoubleArticulated 4 axleRigid 3 axleUtility
Figure 5.6 b Congestion sensitivity at heavy traffic section
81
Figure 5.6 a and b portray that the sensitivity degree of congestion coefficient is high
for heavy commercial vehicles and low for light duty vehicles. In addition, the
degree of congestion sensitivity would be more for highly congested road.
5.3.7 Payload sensitivity
The payload sensitivity is very important in terms of freight modal energy estimation
and comparison. Design Pro* vehicle run simulator suggested a linear relationship
between payload and fuel consumption with an average slope of the line between 165
and 175. This relationship between gross vehicle mass and fuel consumption, derived
from Design Pro* simulation, is used for the energy estimation. Appendix G contains
a sample data set used for deriving the relationship.
The payload sensitivity for B-Doubles was carried out by altering the slope of the
line. The alteration has an effect in the ratio of 1:2 max (20% change in slope of the
line would effect the fuel consumption by 10%). Hence, this shows that payload is
also an important parameter influencing the energy estimation.
5.3.8 Sensitivity summary of road sub-model
The sensitivity study of road sub-model parameters suggested that the error in
estimation coefficients would affect the fuel consumption estimation in the ratio of
1:2 maximum (the error in speed coefficient by 20% would affect the energy
estimation by 10%). This maximum ratio is for error in speed coefficients and
payload slope. The next highest impact is from error in grade coefficient which is in
the range of 1:3.5 (the error in grade coefficient by 35% would affect the energy
estimation by 10%).
The sensitivity analysis carried out above suggested the following order for the
sensitivity of the road sub-model parameters;
i. Speed coefficients and Payload
ii. Grade coefficients
iii. Congestion coefficients; and
iv. Curvature and Roughness coefficients.
_____________________________________________________________________________________ * Design Pro is a vehicle run simulator proprietary to Caterpillar Inc., Peoria, Illinois, USA. They advocate that Design Pro would best specify the Cat engine and the best drivetrain for any application with manufacturer specific product information.
82
The curvature and roughness had shown almost the same magnitude of sensitivity.
Hence, the above discussion depicts that speed coefficients and payload slope are the
most important factor in energy estimation model. Moreover, these parameters would
be in use for the entire movement of the freight. Hence, any errors in these terms are
expected to bring high degree of uncertainty in energy estimation.
The remaining factors do not have high impact on energy estimation process.
Furthermore, these parameters (except roughness) would only be affecting a short
portion of freight movement corridor. Hence, the final comparison result would
experience very small effect of errors in the coefficients of these parameters.
Parameters Change in Parameter (%)
Change in Energy consumption (%)
Speed 20 10
Payload 20 10
Grade 35 10
Congestion 63 10
Curvature 130 10
Roughness 290 10
Table 5. 2 Sensitivity summary of various parameters
5.4 Errors and uncertainty in rail energy estimation
5.4.1 Background
The rail sub-model proposed in Chapter IV is tested for its sensitivity of
• Train length
• Train mass
• Train Speed
• Grade; and
• Curvature
• Number of Locomotives and wagons
The sensitivity of the model estimation coefficients was also scrutinized. A
simplified relationship between the energy influencing parameters is reinstated
below:
[ ][ ][ ]forceCurvatureforceGradespeedrspeedrrSpeed
EfficiencygeneratordieselofnconsumptiofuelSpecificptionFuelConsum
−−×+×+××
×=
)( 2210
Eq. 5.2
83
The remaining energy influencing parameters are fixed for a sensitivity testing of
single parameter. Table 5.3 shows the details of those values and the parameters.
Parameters Sensitivity
Length (m)
Mass (tonnes)
Grade (%)
Curvature (metres)
Speed (km/h)
No. of Loco.
No. of Wag.
Train Length (metres) 3200 Nil Nil 80
Train Mass (Tonnes) 900 Nil Nil 80 1 49
Grade (%) 900 3200 Nil 50 1 49
Curvature (metres) 900 3200 Nil 50 1 49
Speed (km/h) 900 3200 Nil Nil 1 49
Number of Loco 900 3200 Nil Nil 80 49
Number of Wagon 900 3200 Nil Nil 80 1
Table 5. 3 Constant values taken for sensitivity analysis of various parameters
The following sections (5.4.2 to 5.4.7) discuss the rail sub-model’s parameters. It
shows what would be the corresponding fluctuation in the model energy estimation
for a change input parameters values. For in depth understanding of the model
estimation, the sections also discusses the effects of constant coefficients alteration in
energy estimation.
5.4.2 Train Length
Train length is used to quantify the resistive coefficients such as r1 and r2 (see Eq.
5.2). The parameters are believed to determine the resistive forces caused due to
aerodynamics and rolling resistance. The alteration of length from 650 m to 900
metres is portrayed in the Figure 5.7. For the study of train length variation, the rest
of the energy influencing parameters are fixed. The fixed values of the parameters
are tabulated in Table 5.3.
Train Length Sensitivity
2
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
600 650 700 750 800 850 900 950
Length of Train (m)
Effic
ien
cy (
Lt/
1000G
TK
)
Figure 5. 7 Effect of variation in Train Length
84
Train length is also a function of number of wagons and locomotives. Usually the
number of wagons and locomotives determine the number of axles that are presents
in the train consists. Since there is axle load limitation, it is not possible for a very
short train (with less number of axles) to carry a heavy load.
Figure 5.7 shows that with every increase in length of train, there is a decrease in
efficiency. However, this figure might not always describe the practical rail world’s
efficiency. This is because with every increase in train length, a corresponding
increase in mass of the train is expected.
5.4.3 Train Mass
Train mass influence energy consumption of train from various angles. Its main
influence would be in the rolling resistance estimation and coefficient r0. If the track
forces (grade and curve) are also under consideration then mass has a direct affect on
them as well.
Figure 5.8 shows the sensitivity of train mass in energy consumption. The values of
constant chosen for this sensitivity analysis are shown in Table 5.3.
Train Mass Sensitivity
2.7
2.75
2.8
2.85
2.9
2.95
3
3.05
3.1
3.15
3.2
2500 2600 2700 2800 2900 3000 3100 3200 3300
Train Mass (Tonnes)
Fu
el
Eff
icie
ncy (
Lt/
1000 G
TK
)
Figure 5. 8 Effect of variation in Train Mass
Figure 5.8 shows that the efficiency of the movement increases as the mass of the
train increases. It depicts that the train mass is a sensitive parameters in describing
85
the fuel efficiency of the movement. However, the figure only shows the effect of
train mass in energy consumption when train length, number of wagons and
locomotives are kept constant. For the range that Figure 5.8 portrays, the assumption
might hold true. However, when the mass is further increased then there might be the
need of more locomotives and wagons. This is because of the power needed to move
the vehicle and axle load limit to be maintained on the track.
5.4.4 Train Speed
Speed affects the fuel consumption by influencing the fuel flow in the engine and
aerodynamic resistance and others. In fact, speed has been a prominent parameter in
modelling energy consumption since long.
Figure 5.9 shows the sensitivity of train speed in energy consumption. It shows that
as the speed increases the efficiency decreases. It depicts the change in speed is a
sensitive parameter in determining the fuel efficiency of the movement. The values
of constant chosen for this sensitivity analysis are shown in Table 5.3.
Train Speed Sensitivity
0
0.5
1
1.5
2
2.5
3
3.5
4
0 20 40 60 80 100 120
Speed (km/h)
Fu
el
Eff
icie
ncy (
Lt/
1000 G
TK
)
Figure 5. 9 Effect of variation in Train Speed
5.4.5 Grade and curvature
This section discusses the sensitivity of the penalties assigned to route parameters
such as grade and curvature. The values of other parameters chosen for the
sensitivity study of grade and curvature are given in Table 5.3. As the train would
86
usually not run at 80 km/h in high grade and curvature, the speed value was reduced
(to 50km/h) for sensitivity study of route parameters. It is believed that the reduced
values would more resemble the practical ground.
Grade Sensitivity
0
5
10
15
20
25
30
35
0 0.5 1 1.5 2 2.5 3 3.5
Grade (%)
Fu
el
Eff
icie
ncy (
Lt/
1000 G
TK
)
Figure 5. 10 Effect of variation in Route Gradient
Curvature Sensitivity
0
2
4
6
8
10
12
0 500 1000 1500 2000 2500 3000 3500
Curvature Radius (m)
Fu
el
Eff
icie
ncy (L
t/1000 G
TK
)
Figure 5. 11 Effect of variation in Curvature Radius
The sensitivity figures (Figure 5.10 and 5.11) showed distinct characteristics. The
variation due to the grade increment is linear whereas variation due to curvature is
polynomial (of the form - Constant × X-y). This suggests that curvature parameter is
more sensitive as the radius of curvature is less. However, the change in radius from
2800 m to 3000 m is not expected to have significant difference in fuel efficiency.
87
5.4.6 Numbers of Wagons and Locomotives
Numbers of Wagons and Locomotives have a direct impact on length of the train and
number of axles. These parameters play an important role in determining the
coefficients such as r0, r1 and r2.
Due to this complex relationship between number of locomotives and wagons and
train length, the sensitivity of this can not be assessed without further assumption.
However, it is possible to study the sensitivity of the number of axles in the energy
consumption. Figure 5.12 shows the effect of axle number variation in fuel
consumption. As expected, the efficiency of the movement decreases as the frictional
forces increases.
Number of Axles Sensitivity
2.74
2.75
2.76
2.77
2.78
2.79
2.8
2.81
100 105 110 115 120 125 130 135
Number of Axles
Fuel E
ffic
iency (Lt/ 1
000 G
TK
)
Figure 5. 12 Effect of variation of Number of Axles
5.4.7 Sensitivity summary of rail sub-model
This section discusses the relative importance of the parameters mentioned in section
5.4.2 to 5.4.6. The relative importance of parameters are determined by the
corresponding change in energy estimation (percentage) induced due to a pre-defined
change in percentage of in input parameters.
88
Sensitivity comparison
0
20
40
60
80
100
120
140
0 20 40 60 80 100
Percentage change in Parameter
Pe
rce
nta
ge
ch
an
ge
in F
ue
l
Curvature (at 2000 m Radius)
Grade (at 1%)
Length (at 650 m)
Speed (at 30 km/h)
Mass (at 3200 tonnes)
Number of Axles (at 101 axles)
Figure 5. 13 Sensitivity Comparison of various parameters
The degree of sensitivity was found to be varying with percentage change in
parameters. For instance, curvature was most sensitive when percentage change in
parameter is more than 80%. Whereas, curvature was less sensitive when change is
parameter is less than 20%.
When 20% change in parameter was considered as the datum for comparison, the
sensitivity analysis carried out above suggested the following order for the sensitivity
of the rail sub-model parameters.
S.N. Parameters Change in Parameters (%)
Change in Fuel Consumption (%)
1 Grade (at 1%) 20 16.88 2 Length ( at 900 m) 20 15.15 3 Speed (at 30 km/h) 20 7.55 4 Mass (at 3200 t) 20 6.49 5 Curvature (at 2000m) 20 4.65
6 Number of axles (at 101 axles)
20 1.54
Table 5. 4 Sensitivity Comparison
The speed and mass had shown the same magnitude of sensitivity. Moreover, these
parameters would be in use for the entire movement of the freight. Hence any errors
in these terms are expected to bring high degree of uncertainty in energy estimation.
89
Grade showed a high impact on fuel consumption when tested at 1% gradient.
Whereas another route parameter (curvature) did not show high sensitivity at 2000m
radius. But the curvature sensitivity is expected to increase at low radius values,
which is depicted in Figure 5.11.
Train length was also found to alter the fuel consumption estimation significantly.
However, as discussed in Section 5.4.2, train length might have a compound effect
due to increase in mass and number of axles. Hence, though the number of axles
alone did not show significant alteration, but when combined with train length and
mass it would be a significant factor.
5.5 Model Complexity and input data
This study deals with large set of vehicles, both on road and rail. The data
requirement would be high if the complexity of the energy estimation model is
increased. Moreover, any increment in the complexity of the energy estimation
model would demand a higher quality data to match the output value. The shaded
region in Figure 5.1 roughly indicates the working range of the developed model. For
a fixed data quality (which is relatively poor), the model complexity can be limited to
the simpler level, as shown in Figure 5.1, to obtain a superior output.
While it may be difficult to quantify the curves (in Figure 5.1) for the model
developed here, the overall implication is clear: using more complex models with
bad data simply increases the total error in the model. Sighting the scarcity of
adequate set of good quality data and better data error tolerance in simpler model, we
resort to the use of simple model for energy consumption estimation.
Hence the energy consumption model developed in this study is based on the lower
specification measurement parameters. For the model developed, the parameters such
as payload, grade, and alignment curve and vehicle type were believed to have lesser
measurement errors. Hence these parameters were given the higher specification
measurement (compared to other parameters) to improve the model output. This
importance was found to be closely matched with the degree of sensitivity of
parameters affecting the energy estimation.
90
CHAPTER VI CASE STUDY AND MODEL APPLICATION
6.1 Introduction
To demonstrate the application and guide the further development of the proposed
model, a case study corridor has been selected. The area is selected based on the
following criteria:
• inclusion of both rail and road corridor;
• presence of different route alignment characteristics such as grade and
horizontal curvature; and
• representation of a realistic freight carrying route.
This chapter discusses:
• the applicability of the developed comparison model in assessing freight
movement options based on energy consumed; and
• the applicability of the model in evaluating a new corridor development
project based on the energy savings.
6.2 Site description
6.2.1 Background
The Warrego Highway, National Highway A2, links Brisbane with Toowoomba, and
the Darling Downs. The Warrego Highway is a part of the Brisbane-Darwin corridor.
Commencing on Brisbane's western outskirts, the Warrego Highway bypasses the
city of Ipswich to the north before heading in a generally western direction to
Toowoomba. Just east of Toowoomba is the Great Dividing Range commonly
referred as the Toowoomba Range. The highway then crosses through relatively busy
city of Toowoomba (population about 105,302 – 2001 Census) before turning to a
more north-westerly direction crossing the Darling Downs and linking the towns of
Oakey, Dalby, Chinchilla, Miles, Roma and Mitchell before terminating at
Charleville.
91
Most of the Warrego Highway between Brisbane and Toowoomba is 4 lane dual
carriageway. Long term planning and route selection has commenced for a bypass of
Toowoomba.
Toowoomba has a pivotal role in acting as a transport hub for the Darling Downs and
beyond and is an important focal point for interstate and intrastate freight movement,
being at the confluence of the Warrego, New England and Gore Highways
(Maunsell, 1998).
This study focuses on analysing the energy consumed in different freight moving
options (involving road and rail) through Toowoomba. The arbitrary boundaries to
the study area are the junction of Warrego Highway and Paynter Road (east of
Toowoomba) and the junction of Warrego Highway and Nass road (west of
Toowoomba).
Four different options are considered in this study. The options considered are:
1. Existing road route between the junction of Warrego Highway and Paynter
road (east of Toowoomba) and junction of Warrego Highway and Nass road
(west of Toowoomba).
2. Existing railway line between Warrego Highway, Postman Ridge (east of
Toowoomba) and Gowrie junction (west of Toowoomba).
3. Proposed bypass road corridor between the junction of Warrego Highway and
Paynter Road and junction of Warrego Highway and Nass road.
4. Proposed new rail line between the junction of Warrego Highway and
Paynter Road and junction of Warrego Highway and Nass road.
The above options are shown in Figures 6.1 and 6.2. In Figure 6.2, the solid thick
line, passing through Postmans Ridge, Harlaxton and Wetalla, represents the new
proposed rail route, whereas a thin line almost following the Murphys Creek
represents the existing rail line.
92
Figure 6. 1 Road options
Source: Maunsell (1998)
Figure 6. 2 Rail options
Source: QR and QT (2003)
6.2.2 Option One (Existing Road)
A portion between Brisbane to Toowoomba (option involving existing Warrego
Highway section between Postman Ridge Road and Nass Road) is considered in this
section. The following description is based on the road plans provided by
93
Department of Main Roads, Toowoomba district, Toowoomba street index and site
visit. Appendix I contains the detail alignment data extracted from the maps provided
by DMR, Toowoomba.
Alignment description
Towards Toowoomba city (East of the city)
Paynter Road to Flat Gully (Ironbank Gum Wattle) [approx. 1.25 km]
The large portion of the road section has a gradient of around 1.5%. The section also
has a large horizontal curve radius (about 6000m) representing a rather straight road
section.
Flat Gully (Ironbank Gum Wattle) to Connoles Road junction [approx.1.1 km]
The portion of the road contains both ups and downs with a gradient of maximum
3.2% and a minimum 0%. The large section of the road does not have significant
horizontal curvature. However, as the section approaches towards Connoles Road
junction, the horizontal curve radius reaches 3000m, which is the minimum for this
section.
Connoles Road junction to Murphys Creek Road junction [approx.0.7 km]
The road section eases from the horizontal curve having radius of 3000m to a straight
road while moving from Connoles Road junction to Murphy Creek Road junction.
The road remains straight with a gradient of 0.5% max for large part of the section.
Murphys Creek Road junction to Blanchview Road junction [approx.1.2 km]
The road gradient gradually increases in this section till it reaches the maximum of
4.31% and then starts to ease a little with about 2% near the Blanchview Road. The
road section is almost straight throughout.
Blanchview Road junction to Park Ridge Road junction [approx. 0.5 km]
The road gradient eases to nil (or almost zero) towards the west of Warrego Highway
and Blanchview Road junction. Again the grade rises to about 1% just west of Park
Ridge Road junction. However the horizontal curve of the section is negligible.
94
Park Ridge Road junction to west of Roches Road junction [approx. 2.1km]
The road is relatively steep having around 2.5% gradient almost all the way with
maximum of 3.5% gradient near Park Ridge Road junction and at the end of this
considered section (that is about 350m west of Roches Road junction). Road section
is relatively straight with only a single prominent curve present at around the
junction where Jones Road meet Warrego Highway.
West of Roches Road junction to Crossing of East Street [approx. 5.3 km]
This stretch of Warrego Highway is comparatively very windy with steep gradient.
The curvature of the road is in some places as low as 120 m and the maximum
gradient in this section is above 10%.
Segment Location Approx. Distance
Speed (km/h)
Grade Horizontal curvature
1 Paynter Road junction to Flat Gully
1.25 km 100
Around 1.5%
6000 m Radius.
2 Flat Gully to Connoles Road junction
1.1 km 100 Max. 3.2% Min. 0%
Straight section to 3000 m radius.
3 Connoles Road junction to Murphys Creek Road junction
0.7 km 100 Max. 0.5% Min. 0%
Straight section to 3000 m radius.
4 Murphys Creek Road junction to Blanchview Road junction
1.2 km 100 Max. 4.31% Min. 2%
Almost a straight section throughout.
5 Blanchview Road junction to Park Ridge Road junction.
0.5 km 80 Max. 1% Min. 0%
Almost a straight section throughout.
6 Park Ridge Road junction to west of Roches Road junction
2.1 km 60 and 80 at the
end
Max. 3.5% Min. 2.5%
Almost straight section throughout.
7 West of Roches Road junction to crossing of East Street.
5.3 km 100, 80 & 60
(decreases as the road
reaches East Street
junction)
Max. 10%
Min. 120 m Radius Curvy section throughout.
Table 6. 1 Summary of Road characteristics to the east of Toowoomba
95
The simplified grade profile used for energy estimation of this section is shown in
Figure 6.3. The latter shows that the gradient of the section is very prominent and in
some cases the high grade angle may demand 100% more energy than on the plane
road as discussed in Section 4.2.3, Chapter IV and Table 4.4.
Grade profile
(Postman Ridge to Toowoomba)
-2
0
2
4
6
8
10
12
0 2000 4000 6000 8000 10000
Distance (m)
Gra
de (
%)
Figure 6. 3 Grade profile (Postman Ridge to entrance of Toowoomba city)
City Segment (After crossing East Street junction till Nugents Pinch Road)
From the west of the East Street junction, the Warrego highway enters the urban
environment possessing relatively high amount of traffic. The segment of the
Warrego highway within the Toowoomba city is about 11 km long. Within this
segment, there are about 15 signalized intersections and about 42 unsignalized
intersections. Hence the effect of such intersections on fuel consumption for this
segment might be prominent; both due to the decrease in travel speed and increase in
stop/start manoeuvres.
96
Segment Location Approx. Distance
Speed (km/h)
Horizontal curvature
1 East St. Crossing to James St. Crossing
0.9 km 60 (assume)
Two sharp curves and a small section of large radius curve. More than 0.5km of straight section
2 Cohoe St. Crossing to West St. Crossing
3.3 km 60 (assume)
Straight section
3 West St. Crossing to Hursley St. Crossing
1.9 km 60 One curve. Rest of the section is straight.
4 Hursley St. Crossing to Bridge St. Crossing
1.6 km 60 Straight Section. Curve at the end junction.
5 Bridge St. Crossing to McDougall St. Crossing
2.2 km 60 Curve at the start junction. Rest of the section is straight.
6 McDougall St. Crossing to Nugent Pinch Road Junction.
1.5 km 80 Most of the section is straight. Very large radius curves around Nugent Pinch road junction.
Table 6. 2 Summary of Warrego Highway characteristics passing through the
city (towards Nugents Pinch Road)
Outward from Toowoomba city (West of the city)
Nugents Pinch Road junction to Banyula Road junction
The road section has a relatively steep gradient in the beginning and it eases as it
approaches Banyula Road junction. The section has a comfortable horizontal curve
radius of 930m which gets even better as the road reaches Banyula Road junction.
Banyula Road junction to Charlton Connection Road junction
The road section has a continuous grade range from 1.35% to 3.5%. The road is
straight for most of the length, however for a small section there is a horizontal curve
of radius approximately 915m. Overall the change in vertical alignment of the
section is more distinct than horizontal.
97
Segment Location Approx. Distance
Speed (km/h)
Grade Horizontal curvature
1 Nugents Pinch Road junction to Banyula Road junction
0.5 km 80 Min. 0% Max. 2%
Min. 930 m Radius. Straight for most of the section.
2 Banyula Road junction to Charlton Connection Road junction
1.1 km 80 Min. 1.35% Max. 3.50%
Straight for most of the section. Min. 915 m Radius.
3 Charlton Connection Road Junction to Nass Road and Wirth Road Junction
2.8 km 60 Min. 0.5% Max. 6.7%
Strain for most of the section. Min 913 m Radius
Table 6. 3 Summary of Warrego Highway characteristics passing through the city (towards Nash Junction)
The simplified grade profile used for energy estimation of this section is shown in
figure 6.4. The figure shows that there is less steep grade compared to the section of
Warrego Highway coming into Toowoomba from Ipswich.
Grade Profile
(Toowoomba to Nass Road Junction)
-8
-7
-6
-5
-4
-3
-2
-1
0
1
0 500 1000 1500 2000 2500 3000 3500 4000 4500
Distance (m)
Gra
de
(%
)
Figure 6. 4 Grade profile (Exit from Toowoomba city to Nass Road junction)
98
Figure 6.5 shows the simplified speed profile with the grade alignment of the same
section. The speed profile shown in Figure 6.5 was established for a heavy
commercial vehicle. The speed profile was determined based on:
• speed profile of the small car
• speed profile drawing provided by Department of Main Roads, Toowoomba
Districts; and
• analytical judgement.
This speed profile was kept constant through out the fuel estimation analysis. The
main objective of fixation of the speed profile was to standardize the results of
various vehicle runs in the corridor.
Existing road route
0
10
20
30
40
50
60
70
80
0
1249
.68
1962
.3
2956
.56
4206
.24
4899
.92
5474
.92
6575
7615
8261
8900
9510
9863
1033
5
1092
5
1175
0
1917
5
2278
8
2341
3
2386
3
Distance (m)
Sp
ee
d (
km
/h)
-6
-4
-2
0
2
4
6
8
10
12
Gra
de
(%
)
Speed
Grade
Figure 6. 5 Speed and grade profile of existing road route
6.2.3 Option Two (Existing Rail)
The option involves existing rail track between near Postman’s Ridge locality (before
crossing Lockyer creek) and Gowrie Junction. QR (2001) was used to extract the
route alignment data, which provides rail track information of section stretching from
99
Quilpie in the west to Rosewood in the east (the extent of the Brisbane Metropolitan
Area).
The total length of the track between Postman’s Ridge locality and Gowrie Junction
is about 50 km. The track was segregated into 350 segments based on the
homogeneity of horizontal and vertical alignment. Table 6.4 presents the eight broad
sectional divisions carried out to give an overview of the route. The detail data of
horizontal and vertical alignment for the track are in Appendix J.
The track between Postman’s Ridge locality and Toowoomba is a single track
railway. This track climbs up the Great Dividing Range, passing though number of
tunnels before cresting at Harlaxton. From Harlaxton, the track descends to the
Toowoomba CBD.
There are five passing loops on this section namely Lockyer, Murphy’s Creek,
Holmes, Spring Bluff and Rangeview. The maximum allowable speed is 80 km/h,
with block trains restricted to a maximum speed of 60 km/h and triple header block
trains between Harlaxton and Murphy’s Creek, in the Down direction, restricted to a
maximum speed of 20 km/h (QR 2001).
The maximum grade for this section is 2%, when grades on both the direction are
taken into account. The minimum nominal horizontal curve radius for that section is
100 meters.
The track length between Toowoomba CBD and Gowrie junction is about 12 km.
The maximum grade for this section is 1.27%. For most of the length the track has
gradient higher than or about 0.67%. This track segment is relatively windy with
lowest of the curvature measuring around 100m.
The simplified grade profile of the rail track between Helidon –Toowoomba-Gowrie
is shown in Figure 6.6. Table 6.4 shows the sectional running times for two types of
trains currently operating on the track, which are for this study purpose divided into
eight board sections. The given running time do not reflect acceleration and
deceleration characteristics of trains.
100
Grade Profile
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
0 10 20 30 40 50 60
Distance (Km)
Gra
de (
%)
Figure 6. 6 Grade profile of existing rail track
Running time (min)
Freight Mineral
Seg
men
t Location Approx. Distance (km)
Grade Horizontal Curvature (m)
Up Down Up Down 1 Helidon - Lockyer 10 0-1.4 Min. 201 11 12 14 11 2 Lockyer-Murphys Creek 8 0 - 1.3 Min. 201 16 15 16 17 3 Murphys Creek-Holmes 7 0-2 Min. 100 20 20 20 27 4 Holmes-Spring Bluff 7 0-2 Min. 100 17 16 17 22 5 Spring Bluff-Rangeview 10 0-2 Min. 100 22 21 23 29 7 Rangeview-Toowoomba 5 0-2 Min. 100 14 13 18 28 8 Toowoomba-Willowburn 2 0-1.3 Min. 100 10 10 10 12 9 Willowburn-Gowrie 10 0-1.2 Min. 241 14 18 14 20 Table 6. 4 Summary of Rail track characteristics
6.2.4 Option Three (Proposed Road alignment)
The new road route starts from Warrego Highway. It joins the existing four lane
Warrego Highway at Paynters Road, Postmans Ridge, at a grade separated
interchange with Brisbane oriented connection.
The route (under consideration here) ends under the Warrego Highway, Charlton
(where Warrego Highway meets Nass Road and Wirth Road). The total length
between the proposed sections is 28.5km.The location is planned to provide a simple
interchange for all interconnecting movements with the highways.
101
Alignment description
The horizontal curve is comfortable in this option, the tightest of the radius being
600m. The vertical alignment for the section has a maximum grade of 5.5%. A
desirable maximum grade of 4% has been proposed for the route west of range.
The route section is segregated to form 58 homogeneous segments for case study
purpose based on drawing given in Maunsell (1998). The route description followed
hereafter for this new proposed corridor is from Maunsell (1998) and is only divided
into 8 segments. Appendix H contains the alignment data extracted from those
segments.
Segment 1 - Warrego Highway (east) to Murphys Creek Road
The new route joins the existing four lane Warrego Highway at Paynters Road,
Postmans Ridge, at a grade separated interchange with Brisbane oriented
connections. The new west bound carriageway passes below the Warrego Highway
then crosses Rocky Creek and then continue across Postmans Ridge Road and then
linked to Murphys Creek Road.
Segment 2 – Murphys Creek Road to Wards Hill
After crossing Murphys Creek Road, the route would then cross over the ridge north
of Six Mile Creek and proceed to cross a series of gullies and ridges before crossing
the main spur in a deep cutting under the transmission lines at Wards Hill.
Segment 3 – Wards Hill to McNamaras Road
The route would pass over the ridge at Wards Hill and then continue along the
southern base of Wards Hill, across Six Mile Creek and Gittens Road. The route then
ascends westwards on a maximum 5.5% grade to cross Gittens Road and then passes
through Withcott Quarry.
Segment 4 – McNamaras Road to Morleys Road
The route would then follow the northern slopes of the Withcott Valley to commence
its ascent of the Dividing Range.
Segment 5 – Morleys Road to New England Highway
The route cuts under Morleys Road (requiring a new overbridge) then continues the
ascent of the Range on 5.5% grade. From Wallens Road, the route continues to
102
ascend the north slope of the escarpment, on an alignment which follows below the
Southern and Western (Main) Railway, and Blue Mountain Heights residential
estate.
Segment 6 – New England Highway to Bedford Street
The road then completes the ascent of the escarpment by crossing under the New
England Highway in a tunnel. The new road would continue across Old
Goombungee Road, Gowrie Creek and Western Railway. A section of Gowrie Creek
is to be realigned where the road encroaches into the creek. The route then passes to
the south of the Toowoomba City Council’s solid waste landfill area, and on to
Bedford Road.
Segment 7 – Bedford Street to Ganzer Road
The route crosses over Bedford Street on an overbridge, then continues through open
farmland and crosses over Boundary Road on an overbridges. The route then
continues over open fields on a fill embankment, before crossing to the south of
Hermitage Road/Ganzer Road just west of Nugent Pinch Road.
Segment 8 – Ganzer Road to Warrego Highway
The route continues along the gully on the south side of Ganzer Road, continues
through farmland (including several hobby farms) on a gradual grade.
The simplified grade profile of the new proposed road alignment is presented in
Figure 6.7. Table 6.5 gives the summary of new proposed second range crossing.
Grade Profile
-6
-4
-2
0
2
4
6
0 5000 10000 15000 20000 25000 30000
Chainage (m)
Gra
de (
%)
Figure 6. 7 Grade profile Postman Ridge to Charlton (new proposed road
alignment)
103
Segment Location Approx. Distance
Grade Horizontal curvature
1 Warrego Highway(east) to Murphy Creek Road
4.2 km Max. 2.5% Min. 0%
Min. 650 m. Mostly large radius curve (>1200km). and straight section
2 Murphy Creek Road to Wards Hill
2.6 km Max. 4.96% Mostly with average gradient between 2-3%.
Min. 1000 m Mostly curvy with large radius curve (>2000 m).
3 Wards Hill to McNamaras Road
2.7 km
Max. 5.5% Mostly steep with 4-5.5% grade
Min. 650 m Mostly curvy with large radius curve (>1000m)
4 McNamaras Road to Morleys Road
4.8 km
Max. 5.5% Mostly with grade between 1.5– 3.5% grade
Min. 660 m Mostly curvy with large radius curve (>1000 m)
5 Morleys Road to New England Highway
3.3 km
Mostly 5.5% grade.
Min. 600 m Mostly curvy road with 600 (or more) m radius curve.
6 New England Highway to Bedford Street
3.4 km
Max. 4.6% Mostly with grade between 1.5 to 2%
Min. 610 m. Mostly straight large curve radius section (>3000 m).
7 Bedford Street to Ganzer Road
2.8 km
Max. 5.15 % Min. 0 %
Min. 1000 m Mostly straight section
8 Ganzer Road to Warrego Highway
4.5 km
Max. 2.64% Min. 0%
Min. 1000 m Mostly straight section
Table 6. 5 Summary of new proposed second range crossing
6.2.5 Option Four (Proposed Rail)
Maunsell (1998) suggested of building Queensland Rail’s routes in common corridor
where relevant, and the location and size of a freight/industrial terminal with possible
sharing with Queensland Rail. However, in some section of the proposed road
section, the grades are higher than suitable for rail alignment.
Queensland Rail has been undertaking several studies of the Grandchester to Gowrie
Junction corridor with a view to upgrading the route in question. The work has been
done in sections and resolved as far as possible, section by section. The several route
segments have been subjected to preliminary work. The alternative routes between
Helidon and Gowrie Junction are under consideration. The rail route proposed by one
of the QR and QT study (QR and QT 2003) was chosen as the new alternative in this
study. The simplified grade profile of the proposed rail track is shown in Figure 6.8.
104
Grade Profile
1.55
1.57
1.59
1.61
1.63
1.65
1.67
0 5000 10000 15000 20000
Distance (m)
Gra
de (
%)
Figure 6. 8 Grade profile near Lockyer to Gowrie (new proposed rail alignment)
Figure 6.8 suggest that grade profile of the new proposed rail alignment is not very
relaxed. Particularly the short section considered in this study possesses the high
gradient. However, the sharp curves that are present in the existing rail track are
considerably reduced to improve the performance of the train. Appendix K contains
the curvature data of the section which supports the above statement.
6.3 Freight description
Freight in this region is mainly carried by road and rail. Rail has traditionally carried
bulk products such as grain and livestock over relatively long distances but recent
developments have increased the extent of road based transport of these
commodities. Productivity improvements have been achieved through the use of
freight efficient vehicles (road trains and B - Doubles).
The freight task in the region is directed to a wide range of commodities including
bulk grains, livestock, meat products, dairy products, horticultural products
(including flowers), manufactured products (export and import), food items (export
and import) and construction materials (export and import). In addition, to the freight
task generated by the region itself, there is a considerable quantity of freight passing
through the region both interstate and intrastate. Significant quantities of freight to
105
and from the Port of Brisbane pass through the region bound for interstate
destinations such as Melbourne.
There is a diversity of types and quantities of products being carried, not necessarily
in the most efficient manner or mode. There is an expectation that with the
appropriate infrastructure, more efficient mode shares would evolve with consequent
savings to industry and greater safety and convenience on the road network.
This case study focused on the movement of freight described in Table 6.6. The
values used for the comparison were based on the train consists information provided
by Coal and Freight Services Department of Queensland Rail (courtesy: Mr. Mark
Nash) and study carried out at University of Wollongong and Samrom Pty Ltd as a
part of Rail CRC project (courtesy: Prof. Philip Laird).
Freight Type Amount Coal 2000 tonnes Containerized Freight 300 tonnes
Table 6. 6 Freight type
6.4 Energy estimation
This section discusses the estimated energy consumed for each option. The section
considers the fuel consumed in the line haul section of the freight movement for the
purpose of corridor option evaluation of both old and new alignments. However, the
tool is furnished with the subroutine to calculate the fuel consumed for pick up and
delivery legs as well. As discussed in Chapter III, this is essential for determining the
actual energy advantage that one freight moving option has over other. This inclusion
of energy consumed in pick up and delivery section demands more details of pick
and delivery route legs and also the vehicle types. The tool users are left to decide on
those factors to compare door-to-door modal efficiencies.
6.4.1 Option one (Existing road)
B-Doubles and semi-trailers are the widely used freight moving vehicles in the
existing road route. Both of these vehicles were considered for in the case study. The
106
vehicle characteristics of a representative B-Double and Semi-trailer are presented in
Appendix B.
The existing road section was divided into 73 sections while approaching the
Toowoomba city from Postman Ridge. The city section runs for about 11 km and
was discussed earlier in Section 6.2.2. To represent the congestion of the city run, the
volume to capacity ratio for this section was taken as 0.6. The section of the road
exiting from the city is about 1.5 km and was divided into 14 sections according to
grade and curvature of the section.
B Double
Figure 6.9a and Figure 6.9b show the estimated fuel performance of a B Double.
Depending upon the payload, the total number of runs varies which directly affect
the total fuel consumption. Although the absolute fuel consumption increased with
increase in payload, the fuel consumption performance also showed the increment.
For a freight task of 300 tonnes (see: Table 6.6), the total fuel consumed by a B-
Double is demonstrated in Figure 6.9b. The latter shows the improvement in loading
(payload) from 58% to 98% induced an improvement in fuel consumption by 35%
(the base-case being fuel consumed for 58% loading).
As discussed in section 6.2.2, the segment of road entering Toowoomba (Postman
Ridge to Toowoomba Run; 11.372 km) is very windy and steep. This is reflected on
the fuel performance shown in Figure 6.9a, in which (for every loading condition)
fuel performance of Postman Ridge to Toowoomba run is less compared to other two
runs. Although the grade was assumed to be absent in the city run, the fuel
performance is low compared to the performance of the section coming out of the
city. This difference in performance is the result of high congestion on fuel
consumption while driving within the city area. If fuel performance (Lt./1000 NTK)
while moving out of the city (Toowoomba to Gowrie Junction Run) is taken as 1,
then the ratio of fuel consumed in between Out of City Run, City Run and Entering
City Run would be approximately 1:1.2:1.4, respectively (i.e. 20% and 40%
increment respectively).
107
Figure 6. 9a B Double Performance Chart (A)
Figure 6. 9b B Double Performance Chart (B)
Six Axles Articulated Truck
Figure 6.10a and Figure 6.10b show the estimated fuel performance of a Six-Axle
Articulated Truck. For a freight task of 300 tonnes (see: Table 6.6), the total fuel
consumed by the Six-Axle Articulated Truck is demonstrated in Figure 6.10b. The
latter shows the improvement in loading (payload) from 58% to 98% induced an
108
improvement in fuel consumption by 32.2% (the base-case being fuel consumed in
58% loading).
The fuel performance of Postman Ridge to Toowoomba run is less compared to other
two runs for Six-Axle Articulated Truck. Due to the congestion penalty, the fuel
consumption while driving within the city area is high compared to Toowoomba to
Gowrie Junction Run regardless of no grade assumption. If fuel performance
(Lt./1000 NTK) while moving out of the city (Toowoomba to Gowrie Junction Run)
is taken as 1, then the ratio of fuel consumed in between Out of City Run, City Run
and Entering City run would be approximately 1:1.2:1.5, respectively (i.e. 20% and
50% increment respectively).
Figure 6. 10a Six Axles Articulated Truck Performance Chart (A)
Figure 6.10b Six Axles Articulated Truck Performance Chart (B)
109
Table 6.7 compares the performance of the two road freight vehicles considered. The
tabulated values are the performance of respective vehicles on the existing road
route.
Payload 58% 76% 98%
Description B - Double
Articulated Truck
B - Double
Articulated Truck
B - Double
Articulated Truck
Freight moved (tonnes)
25 17.65 33.33 23.08 42.86 30
Fuel Consumed in a single run (Lt.)
22.44 17.03 23.71 18.20 25.15 19.70
Total Efficiency (Lt./ 1000 NTK)
33.35 35.86 26.43 29.30 21.80 24.40
Table 6. 7 Comparison table (existing road) Table 6.7 shows that to move a small amount of load, choosing a smaller vehicle
would be advantageous. For example, when there is a 25 tonnes to be transported
then use of 6 Axle Articulated Truck would give better than 30 Lt./1000 NTK where
as use of B Double would only give approximately 33 Lt./1000 NTK. In such case,
use of 6 Axle Articulated Truck would prove beneficial for an energy prospective.
6.4.2 Option two (Existing rail)
Typical train consists running on the Helidon to Gowrie Junction track are presented
in Table. 6.8. The latter is based on the information gathered from Queensland Rail
and the simulation by Mr. Max Michell of Samrom Pty Ltd Adelaide (Personal
communication with Prof. Phil Laird).
Train Type Locomotives Wagons
Approx
Weight (Tonnes)
Approx
Length (Meters)
Approx
Coal Train (loaded) 2 40 2,644 670
Coal Train (Empty) 2 40 680 670
Container Train 2 40 1800 640
Primary Industries(Loaded) 2 31 1,931 520
Primary Industries(Empty) 2 31 524 520
Table 6. 8 Train consist information
110
The existing rail track under consideration was divided into 273 sections, which
includes the track segments between Murphy Creek and Gowrie Junction via
Toowoomba. The length of track under consideration for entering the city of
Toowoomba is approximately 30 km and the length of track exiting the city to
Gowrie Junction is approximately 12 km. The energy intensity of rail freight was
found high compared to values suggested by previous studies for other corridors.
The difference could be the result of hilly terrain of the study area, which affects the
track forces (grade and curvature) and mass carrying ability. Moreover, there is also
the restriction imposed on the length of the train (due to crossing loop length) which
would adversely affect the load carrying capacity.
The fuel performance of the existing rail is shown in Table 6.9.
Train Properties
Section (Approx.)
Fuel Used (Litres)
Distance Travelled (km)
Efficiency (Lt./1000 NTK)
Train Length = 640m
Helidon to Murphy Creek
238.55
17.89
10.07
Train Mass =1800 ton
Murphy Creek to Spring Bluff
537.34
20.08
20.12
Gross to Net Ratio = 1.36
Spring Bluff to Gowrie Junction
395.57
21.44
13.94
Total 1171.46 59.41 14.90
Table 6. 9 Fuel performance on the existing rail track
As discussed in chapter three, the efficiency varies considerably with train properties.
The train properties used for performance computation in this study is tabulated in
the first column of Table 6.9.
The first section (distance 17.89 km) is the distance between Helidon to Murphy
Creek. Since this section has less curve and relatively relaxed grade, the fuel
performance of this section is better compared to the second and third sections, as
shown in third and fourth rows of table 6.9.
The second section (distance 20.08 km) is the distance between Murphy Creek to
5.11km away from Spring Bluff. This section has considerable grade and curvature
111
as could be seen on Section 4.2.3. These constrain in curvature and high grade is also
reflected in the calculated efficiency in Table 6.9.
The third section (distance 21.44 km) is the distance between 5.11km away from
Spring Bluff to Gowrie Junction. This section is less curvy and has less grade (refer
Section 4.2.3). Hence the performance is better in this section.
The simulation by Mr Max Michell of Samrom Pty Ltd. Adelaide gave a similar
result. The result for a 670m long two locomotive train, carrying 2000 tonnes, was
around 909 litres. The difference in the results could be due to the variation in the
length of track (around 2km); the slight variation in the interpretation of the
alignment profile; differences in train properties assumed. The graphical
representation of this comparison is presented in Figure 6.11.
Figure 6. 11 Simulation Performance Comparison
6.4.3 Option Three (Proposed Road alignment)
The proposed road alignment possesses less horizontal curvature and the gradient is
very little compared to the existing road. However, the length of road under
112
consideration in the proposed road alignment is almost equal to the length of the
existing road alignment.
Unlike in the existing road section, the road length was not divided into city and
outskirts section here. This is because the new proposed road does not pass through
the city section. The speed profile of the road section was assumed based on
proposed grade and curvature of the section. The speed profile was kept constant for
both types of vehicles considered.
The efficiency of for the three different payload condition under consideration is
presented in Table 6.10. The overall efficiency for 98% payload and 58% payload
was found to improve by 6.6% and 7.7% corresponding to the improvement in road
alignment (based on existing road efficiency).
Figure 6. 12 Fuel performance on new proposed road route
113
Payload Vehicle Description 58% 76% 98%
Weight moved (tonnes) 17.65 23.08 30 Fuel Consumed in a single run (Lt.) 16.07 17.27 18.80
6 Axles Articulated Truck
Total Efficiency (Lt./ 1000 NTK) 33.10 27.21 22.78
Weight moved (tonnes) 25 33.33 42.86 Fuel Consumed in a single run (Lt.) 21.11 22.40 23.88
B Double
Total Efficiency (Lt./ 1000 NTK) 30.70 24.44 20.26
Table 6. 10 Comparison table (proposed road)
6.4.4 Option Four (Proposed new Rail)
The proposed new rail has more relaxed curvature desirable for the smooth running
of long train. However, the section under consideration here mainly consists of high
gradient. The travel distance between the places has been considerably reduced.
Because of this, the absolute amount of fuel saved would be important measurement
regardless of the apparent decrease in energy efficiency (measured in terms of
Lt./1000 NTK). The energy efficiency (measured in terms of Lt/tonne moved) is also
expected to improve because of the relaxation of limiting train length and increased
load carrying capacity due to favourable alignment.
During the study of Rail CRC Project 24, Samrom Pty Ltd and Phil Laird suggested
that the following train (refer Table 6.11) would be able to run on the new proposed
rail alignment. However, operation of the train (refer Table 6.11) would not be
possible on the existing rail track due to length and speed restriction. The
performance showed in Table 6.11 portrays that there is not essentially a huge gain in
efficiency when measured in terms of Lt./1000 NTK.
Number of Locomotives
Trailing Load
Train Length Speed
Train
Properties 2 3000 Tonne 1250 m 100 km/hr Fuel Used (Litres)
Distance Travelled (km)
Efficiency (Lt./1000 NTK)
Run
Performance 1089 20.09 22.54
Table 6. 11 New track’s train properties and performance
This lack in expected large gain in the efficiency term could be attributed to:
• Insignificant improvement in the proposed grade of the track due to the
nature of the terrain. (refer: Figure 6.6 and 6.8); and
• Increased running speed.
114
However, the absolute improvement in the fuel efficiency (measured in lt/1000
tonnes moved) is noted to have improved, when freight movement between near
Lockyer and Gowrie is considered for both the rail options. This could be attributed
to the factors mentioned in the first paragraph of this section (6.4.4). This
improvement is portrayed in Figure 6.13.
Figure 6. 13 Rail performance: Old Rail Route versus New Rail Route
Note: Fuel used presented above is for a single run. In new route a single run was assumed to have capacity to carry approximately 1075 tonnes more.
6.4.5 Options Comparison
The options and performances discussed in section 6.4.1 to 6.4.4 have been
summarized in this section. The starting and ending points of all the options involved
do not exactly overlap with each other. Hence the comparison shown below should
only be treated as a preliminary analysis and suggestion.
Movement of 2400 tonnes of containerized freight has been considered in the Table
6.12 for the comparison purpose.
115
Efficiency Vehicle Route Distance
(km) Number of Runs Lt./1000 NTK Lt./1000 Ton
Fuel (Lt.)
Articulated Truck 80 24.40 656.67 1576 B Double
Existing Route
24.53 56 21.80 586.25 1409
Articulated Truck 80 22.78 626.67 1504 B Double
Proposed Route
28.5 56 20.26 557.50 1338
Old Route Train Existing 50 2 14.90 807.54 2140 New Route Train Proposed 20 1 22.54 453.75 1089
Table 6. 12 Four options comparison
Table 6.12 shows that the existing trains as the efficient mode (amongst the
comparison) of moving the freight when compared in terms of litres per 1000 tonnes.
However, when the absolute expected fuel gain is considered, the new trains is found
as the most efficient mode and the existing trains as the least efficient one. This is
depicted in Figure 6.14 below.
Figure 6. 14 Four options comparison
In the existing condition, B Double operation has been found to be more
energetically beneficial than train and articulated trucks. However, while comparing
between the two trucks, the load factor plays as important role as discussed in section
6.4.1.
116
CHAPTER VII MODEL APPLICATION: Simulated Cases
7.1 Background
Some simulated routes have been considered in this chapter to portray the extended
application of the model developed in this thesis. The virtual routes were planned so
as to take into account the effect of gradient, travel speed, curvature and the handling
of the freight at the intermodal station in a realistic way.
The options consist of road and rail line hauling accompanied with road pick-up and
delivery. The routes described below are arbitrary and may not exactly resemble any
actual freight corridors. However, the virtual routes were developed to closely reflect
real-world scenarios. The routes were developed based on the concepts shown in
Figure 7.1 and 7.2.
Figure 7. 1 Intermodal freight movement concept
Figure 7. 2 Road alone freight movement concept
Rail Link
Freight Depot
Intermodal Terminals
Freight collection and distribution route
Road link
For Pick Up and Delivery
Road Link
Freight Depot
Freight collection and
distribution route
117
Freight collection and distribution route has been assumed to be same in both the
cases (road alone and intermodal). These routes might comprise of city run and use
of smaller road vehicle, but would be same for both. Hence the energy consumed in
these legs is not discussed in the comparison process.
7.2 Route specification and comparison scenarios
This section discusses the characteristics of the virtual corridors used in the model
application. This study resorted to five hypothetical freight corridors to illustrate the
use of energy comparison model. The general characteristics of the freight routes are
given in Table 7.1.
Length (km)
Corridor No. Pick-up Road Link Rail Link Delivery Road Link No. of Intermodal terminals
1. 50 700 50 2
2. 100 600 100 2
3. 150 500 150 2
4. 200 400 200 2
5. Road Alone movement (Length 800 km) 0
Table 7. 1 Freight routes general characteristics
The route alignments were fixed so as to develop a fair comparison between
scenarios. Table 7.2 presents the route alignment; the detail breakdown of this
alignment is presented in Appendix L.
Percentage of total link (%) Geometric Properties Rail Line Haul Road Line Haul Road Pick-up and Delivery
Grade 10 10 12
Curvature 10 10 12
Grade + Curvature 5 5 8
Straight Section 75 75 68
Table 7. 2 Alignment properties of hypothetical corridors
Each link was segregated into several homogeneous sections. As shown in appendix
L, the length of each homogenous section had been determined throughout the
analysis as a percentage of total route distance to simplify the comparison process.
Similarly, the roughness of the road surface had been fixed to 100 NRM counts per
km and the volume capacity ratio had been fixed at 0.3 for all on road movements.
The simulated case-studies are further categorized depending upon the operational
characteristics of freight movements. They are categorized based on:
118
i. Type of vehicle used: a)6 axle articulated truck; b)B-Double; c)Train Type
ii. Payload ratio The payload ratio of road vehicles was varied to illustrate the expected effect of
payload on trip energy demand. Three standard types of trains were used to
determine the effect of variation in train properties. The train types used for the
comparison are shown in Table 7.3.
Train Type Properties Type A Type B Type C
Length of Train (m) 800 1000 1200 Mass of Train (tonnes) 2500 3200 3500 Gross to Net Ratio 1.8 1.7 1.6 Number of Locomotives 2 2 2 Number of Wagons 32 40 50 Total Number of Axles 144 176 216 Net Weight Carried (tonnes) 1389 1882 2188
Table 7. 3 Train Properties
Based on those operational characteristics and routes mentioned above, the model
was run for 28 scenarios (Refer Table 7.4) and the outputs are discussed briefly in
section 7.3.
119
Operational Characteristics
Scenario Number
Road Leg length (km)
Vehicle Train leg length (km)
Payload (%)
Train Type
1 100 6Axle Artic. 700 100 A
2 100 6Axle Artic. 700 100 B
3 100 6Axle Artic. 700 100 C
4 100 B Double 700 100 A
5 100 B Double 700 100 B
6 100 B Double 700 100 C
7 200 B Double 600 80 A
8 200 6Axle Artic. 600 80 A
9 200 B Double 600 80 B
10 200 6Axle Artic. 600 80 B
11 200 B Double 600 80 C
12 200 6Axle Artic. 600 80 C
13 300 B Double 500 80 A
14 300 6Axle Artic. 500 80 A
15 300 B Double 500 80 B
16 300 6Axle Artic. 500 80 B
17 300 B Double 500 80 C
18 300 6Axle Artic. 500 80 C
19 400 B Double 400 80 A
20 400 6Axle Artic. 400 80 A
21 400 B Double 400 80 B
22 400 6Axle Artic. 400 80 B
23 400 B Double 400 80 C
24 400 6Axle Artic. 400 80 C
25 800 B Double NA 80 NA
26 800 6Axle Artic. NA 80 NA
27 800 B Double NA 100 NA
28 800 6Axle Artic. NA 100 NA
Table 7. 4 List of Scenarios
These scenarios had been developed allowing the road link to meet the rail line-haul
at different points, in order to quantify the energy impacts of each option. It is
acknowledged that the operation of B-Double on pick-up and delivery links could be
restricted by factors such as operational permission of long and heavy vehicles on
certain road type and time of day. For the operation of any type of vehicle, the final
freight depot centre should have been designed for the full operation of that vehicle
type, especially for easy access and turning of long vehicles. Hence the operation of
B-Doubles could be only for comparison purpose in some scenarios presented in
Section 7.4, particularly when the pick-up and delivery legs are short in length and
comprises of some urban movement.
120
7.3 Energy Estimation
This section presents the energy demand of each scenario listed in Table 7.4 (in
Section 7.2). The road and rail link length varies across the scenarios. Furthermore,
there is also a change in alignment properties between various types of links such as
road line-haul, rail line-haul and road pick up and delivery. This section also
discusses the energy demand for each of those sections.
7.3.1 Scenario one to six (route remain constant with varying vehicle properties)
Scenarios one to six operate on the same route. The variations across these scenarios
are the type of road vehicles and the type of train in operation.
Scenario one and four has the same type of train and similarly scenario two and five
and scenario three and six also have the same type of train. The difference between
these paired scenarios is the type of road vehicle (Articulated Truck or B-Double)
serving road pick-up and delivery. However, both types of road vehicles are assumed
to be operating on full loading capacity in these six scenarios.
The performance of B-Double and Articulated Truck on the road pick-up and
delivery link are presented in Figure 7.3.
Figure 7. 3 Performance of road vehicles on pick-up and delivery links
121
The first eight bars in Figure 7.3 shows the fuel consumed on different section of
road pick-up and delivery links. However, the last two bars on the right hand corner
shows the fuel efficiency of those run which incorporates the freight being moved
along with distance travelled.
The fuel consumption portrayed in Figure 7.3 is for a single run of the vehicle on
pick-up and delivery links. Due to variation in load carrying capacity of a train, the
number of trips made in the road pick-up and delivery leg would vary to match the
realistic payload limit of the train. The total fuel consumed in these six scenarios is
presented in Figure 7.4.
0
2000
4000
6000
8000
10000
12000
14000
16000
Sce
nario
One
Sce
nario
Four
Sce
nario
Tw
o
Sce
nario
Fiv
e
Sce
nario
Thre
e
Sce
nario
Six
Fuel C
onsum
ption (L
t.)
Road Pick up and Delivery Intermodal Transfer Rail Line Haul
Figure 7. 4 Total fuel consumed for scenario one to six
The total fuel consumed portrayed in Figure 7.4 does not depict the efficiency of the
scenario. The efficiency is depended on amount of freight being transferred as well.
In these scenarios, one and four has the least freight moving capacity and three and
six has the highest freight moving capacity. Table 7.5 presents the freight being
moved in scenario one to six.
Scenarios Train Type in Use Freight moved (Tonnes)
Scenario One and Scenario Four Type A 1389
Scenario Two and Scenario Five Type B 1882
Scenario Three and Scenario Six Type C 2188
Table 7. 5 Freight moving capacity of scenario one to six Table 7.5 and Figure 7.4 could be used to derive the efficiency of the total movement
across the six scenarios. The aggregate fuel performance across those six scenarios is
presented in Figure 7.5.
122
Figure 7. 5 Aggregate fuel performance (Scenario one to Scenario six)
Figure 7.5 illustrates that scenario six is efficient compared to scenario one to five.
Scenario six has B-Doubles operating on pick-up and delivery leg which is 100km
and Type C train hauling the freight over 700km corridor. It portrays that even with
Type C train in operation, if the pick-up and delivery links are served by Articulated
Trucks then the overall performance would be poorer compared to Type B train with
B-Doubles operating on pick-up and delivery links.
7.3.2 Scenario seven to twelve (route remain constant with varying vehicle properties)
Scenario seven to twelve operates in 600 km long rail line-haul and 200 km long
road pick-up and delivery corridor; with 80% payload in two road vehicle categories
namely, B Double and Articulated Truck. Furthermore, three different train types
were considered to illustrate the affect of variation in train properties.
As shown in Table 7.4, scenario seven and eight operates in the same line-haul
environment and hence the variation in total energy efficiency would illustrate the
difference in performance of B-Double and Articulated Truck in pick-up and
delivery link. Similarly, scenarios nine and ten operates in the same-line haul
operating conditions and likewise scenarios eleven and twelve have the same line-
haul condition.
The performance of B-Double and Articulated truck in 200km long pick-up and
delivery section considered here are presented in Figure 7.6.
123
Figure 7. 6 Road vehicle performance with 80% payload on 200 km road
Figure 7.6 shows that Articulated Truck consumes less fuel in each section of road.
However, the load carrying capacity of Articulated Truck is less compared to B
Double. Hence B-Double has higher efficiency than Articulated truck as shown in
the right hand corner of Figure 7.6.
Similarly, the fuel consumed by three different types of train (Type A, B and C) on
600 km long rail corridor for a single run is shown in Figure 7.7.
0
1000
2000
3000
4000
5000
6000
Str
aig
ht
Se
ctio
n
Gra
de
+
Cu
rve
Se
ctio
n
Gra
de
Se
ctio
n
Cu
rve
Se
ctio
n
Fu
el C
on
su
mp
tio
n (
Lt.
)
Type A Train
Type B Train
Type C Train
Figure 7. 7 Train performance in 600 km rail link
Although Figure 7.7 shows that Type A train consume less energy, Type C train are
more efficient when freight moved is also taken into consideration (Refer Figure
124
7.8). The total load carrying capacity of different train types are presented in Table
7.5 (Section 7.3.1).
6
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7
Train Type (A, B and C)
Effic
iency (
Lt./1
000 N
TK
)
Type A Train Type B Train Type C Train
Figure 7. 8 Efficiency of three train types on 600m rail line haul link
The total fuel consumed by scenarios seven to twelve are presented in Figure 7.9. It
shows scenario twelve consume the highest amount of energy.
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Scenario 7
Scenario 8
Scenario 9
Scenario 1
0
Scenario 1
1
Scenario 1
2
Fu
el
Co
nsu
mp
tio
n (
Lt.
)
Road Pickup and Delivery Intermodal Transfer Rail Line Haul
Figure 7. 9 Total fuel consumed in scenario 7 to scenario 12
The increment in total fuel consumed between scenario seven and scenario twelve is
about 6978 lt (58% increment compared to scenario 7); and the increment in net
freight mass being moved is 799 tonnes (about 57.5% compared to scenario 7). This
125
indicates almost one to one increment between fuel consumption and tonnes moved
when compared in percentage terms.
10
10.2
10.4
10.6
10.8
11
11.2
11.4
Scenario 7 Scenario 8 Scenario 9 Scenario 10 Scenario 11 Scenario 12
Eff
icie
ncy (
Lt.
/1000 N
TK
)
Figure 7. 10 Energy efficiency between scenario 7 and 12
Scenario seven, nine and eleven is served by B-Doubles on road pick-up and delivery
links. When total energy efficiency between scenario seven and eleven is compared,
scenario eleven is efficient. The improvement in energy efficiency between scenario
seven and eleven is 0.44 lt/1000 NTK (which is about 4.1% improvement compared
to scenario seven efficiency).
Scenario seven efficiency showed better performance compared to scenario twelve.
This is because of the difference in operating efficiency of road freight moving
vehicles. The performance of B Double with Type A train (scenario seven) was
found to be more efficient than the performance of Articulated Truck with Type C
train (scenario twelve). When compared individually, Type C train is efficient
compared to Type A train (Refer Figure 7.8).
7.3.3 Scenario thirteen to eighteen (route remain constant with varying vehicle
properties)
This section presents the performance of freight moving vehicles when the combined
length of road pick-up and delivery leg is 300km and rail line hauling length is
500km. Vehicles used on road pick-up and delivery are B-Double and Articulated
Truck. The payload of these vehicles has been simulated at 80% of total capacity.
126
Hence, payload for B-Double and Articulated Truck in these scenarios would be 35
tonnes and 24.5 tonnes, respectively.
Scenarios thirteen and fourteen would use Type A train and similarly scenarios
fifteen and sixteen would use Type B train, and scenarios seventeen and eighteen
would use Type C train.
0
5000
10000
15000
20000
25000
Scenario 1
3
Scenario 1
4
Scenario 1
5
Scenario 1
6
Scenario 1
7
Scenario 1
8
Fu
el
Co
ns
um
pti
on
(L
t.)
Road Pickup and Delivery Intermodal Transfer Rail Line Haul
Figure 7. 11 Total fuel consumed in scenario 13 to scenario 18
Figure 7.11 shows the total fuel consumed by scenarios thirteen to eighteen. The
scenario eighteen has the high energy consumption due to large amount of freight
being transferred compared to scenario thirteen (or Scenario 17).
Between scenario thirteen to eighteen, the road pick-up and delivery fuel
consumption comprises of larger portion of total fuel consumption. However, the
actual distance travelled by rail-line haul is 1.67 times higher than total of road pick-
up and delivery leg.
12
12.2
12.4
12.6
12.8
13
13.2
13.4
Scenario 13 Scenario 14 Scenario 15 Scenario 16 Scenario 17 Scenario 18
Effic
iency (
Lt./1
000 N
TK
)
Figure 7. 12 Energy efficiency between scenario 13 and 18
127
This section also shows that B-Double when used with Type C train would provide
the most efficient freight moving option.
7.3.4 Scenario nineteen to twenty-four (route remain constant with varying
vehicle properties)
Scenario nineteen to twenty-four operates on 400 km long rail-line haul and 400km
road pick-up and delivery legs; with 80% payload for both road vehicles. Three types
of train discussed above carry freight on rail line-haul link.
Figure 7.13 illustrates the variation in total energy consumption across scenarios
nineteen to twenty-four. It portrays the step pattern increment in total energy
consumption. Although the length of road and rail legs is equal, the road fuel
consumption comprises of between 73% and 76% of total fuel consumption.
0
5000
10000
15000
20000
25000
30000
Scenario 1
9
Scenario 2
0
Scenario 2
1
Scenario 2
2
Scenario 2
3
Scenario 2
4
Fu
el
Co
ns
um
pti
on
(L
t.)
Road Pickup and Delivery Intermodal Transfer Rail Line Haul
Figure 7. 13 Total fuel consumed in scenario 19 to scenario 24
13
13.5
14
14.5
15
15.5
Scenario 19 Scenario 20 Scenario 21 Scenario 22 Scenario 23 Scenario 24
Effic
iency (
Lt./1
00
0 N
TK
)
Figure 7. 14 Energy efficiency between scenario 19 and scenario 24
128
Figure 7.14 depicts the total fuel performance of scenarios between nineteen and
twenty-four. The difference in energy efficiency between Scenario twenty-one and
twenty-three is much less. This could be attributed to low contribution of rail fuel
consumption (on total fuel consumption).
7.3.5 Scenario twenty-five to twenty-eight (Road alone movements)
This section discusses the fuel performance of scenarios on which freight moves on
road only. Two types of road vehicle are considered with varying payload (80% and
100%). The total freight moving distance was fixed to 800 km. The alignment for
road line-haul movement was considered more relaxed compared to road pick-up and
delivery link (Refer Table 7.2 in Section 7.2).
The performance of road vehicles on 800km long road line-haul is shown in Figure
7.15.
0
50
100
150
200
250
300
350
400
450
Articulated Truck B Double Articulated Truck B Double
80% Payload 100% Payload
Fuel C
onsum
ptio
n (
Lt.)
Straight Section Grade + Curve Section Grade Section Curve Section
Figure 7. 15 Fuel Performance of road vehicle on road line-haul link The efficiency of road vehicles is presented in Figure 7.16. As expected, it shows
that Articulated Truck would be more energy efficient when used in full capacity
compared to B-Double being used on less capacity.
129
0
5
10
15
20
25
Articulated Truck
(Scenario 25)
B Double
(Scenario 26)
Articulated Truck
(Scenario 27)
B Double
(Scenario 28)
80% Payload 100% Payload
Fuel E
ffic
iency (
Lt./1
000 N
TK
)
Figure 7. 16 Efficiency of road alone haulage
7.4 Overall results
The model results presented above for different scenarios illustrates the better
efficiency of intermodal freight movement option compared to road alone movement.
Furthermore, for the road pick-up and delivery movement the efficiency of the
scenarios improved with improvement in the payload ratio for road vehicles.
6
8
10
12
14
16
18
0 2 4 6 8 10 12 14 16
Rail leg length : Road leg length
Tota
l T
rip E
ffic
iency (
Lt.
/1000 N
TK
)
B Double (100% Payload)
B Double (80% Payload)
B Double (60% Payload)
Articulated Truck (100% Payload)
Articulated Truck (80% Payload)
Articulated Truck (60% Payload)
Figure 7. 17 Fuel efficiency for various combinations with Type A Train
Figure 7.17 illustrates the fuel efficiency of total trip when road pick-up and delivery
length varied to form a different proportion of total trip length. The later illustrates
the results of simulated trips of road vehicles operating in conjunction with Type A
130
train (carrying 1389 tonnes of freight). It depicts that when rail leg length is 15 times
longer than road pick-up and delivery leg then trip fuel efficiency would improve
approximately by 1.7 to 2.1 times (compared to the efficiency of total trip when road
and rail leg is equal).
Similarly, Figure 7.18 shows the simulated trips carrying 1882 tonnes of freight with
operation of Type B train on rail line-haul. It shows that improvement in fuel
performance in total freight trip when there is an increment in rail portion of the trip.
As expected, the trip comprising Articulated Truck with 60% payload provided the
worst case between the scenarios compared. The overall fuel performance
improvement, due to variation in rail line-haul portion, ranged from 1.7 to 2.2 times
(compared to the efficiency of total trip when road and rail leg is equal).
6
8
10
12
14
16
18
0 5 10 15
Rail leg length : Road leg length
Tota
l T
rip E
ffic
iency (
Lt.
/1000 N
TK
)
B Double (100% Payload)
B Double (80% Payload)
B Double (60% Payload)
Articulated Truck (100% Payload)
Articulated Truck (80% Payload)
Articulated Truck (60% Payload)
Figure 7. 18 Fuel efficiency for various combinations with Type B Train
131
6
8
10
12
14
16
18
0 5 10 15 20
Rail leg length : Road leg length
To
tal T
rip
Eff
icie
ncy
(L
t./1
00
0 N
TK
)
B Double (100% Payload)
B Double (80% Payload)
B Double (60% Payload)
Articulated Truck (100% Payload)
Articulated Truck (80% Payload)
Articulated Truck (60% Payload)
Figure 7. 19 Fuel efficiency for various combinations with Type C Train
Figure 7.19 shows the fuel performance of scenarios operating with Type C train in
rail line-haul movement. The later shows the performance of simulated cases with
2188 tonnes of freight movement. The operation of 100% loaded B-Double in
combination with Type C train showed the best performance, whereas, 60% loaded
Articulated Truck in operation with Type C train showed the worst performance
between the scenarios compared in Figure 7.19
Amongst the entire intermodal simulated cases;
• operation of full loaded B Double with Type C train has shown the best
performance; and
• operation of 60% loaded Articulated Truck with Type A train has shown the
worst performance.
However, in most of the cases road alone movements with low payload ratio showed
even poorer performance than the worst intermodal scenario. Whereas, fully loaded
B Double in a road alone movement showed a better fuel performance than
combination of 60% loaded Articulated Truck operating in conjunction with Type A
train when road pick-up leg and rail line-haul leg were equal in length.
132
The typical order of intermodal simulated cases when sorted according to the fuel
performance (in decreasing order) is:
i. Intermodal movement with fully loaded B-Double on road pick-up and
delivery;
ii. Intermodal movement with fully loaded Articulated Truck on road pick-up
and delivery;
iii. Intermodal movement with 80% loaded B-Double on road pick-up and
delivery;
iv. Intermodal movement with 80% loaded Articulated Truck on road pick-up
and delivery;
v. Intermodal movement with 60% loaded B-Double on road pick-up and
delivery; and
vi. Intermodal movement with 60% loaded Articulated Truck on road pick-up
and delivery.
133
CHAPTER VIII CONCLUSIONS AND FUTURE RESEARCH
8.1 Literature review
After comprehensive literature review, the thesis reported the very significant
proportion of energy being utilized on land-based freight transport sector all over the
world. The review of energy consumed by various transport modes highlighted the
rapid increasing trend in road freight energy consumption along with its rise in
market share.
A complete freight task could involve more than one mode and various combination
options. This involvement of more than one mode warranted different phases in
energy consumption, along with different modes used. Models developed for
estimating the energy consumption for rail, heavy commercial vehicles and light
commercial vehicles were extensively reviewed and grouped based on their
modelling approach.
The literature review explored energy quantification procedure on each segments of a
complete freight task. Hence, the research aimed to compare and quantify the energy
advantage that one option would have on another.
8.2 Model development and sensitivity of model parameters
A complete freight task was divided into four segments for the total energy
estimation purpose. These segments are:
i. Energy consumed in Pick-up leg of the task;
ii. Energy consumed in Line-haul link of the task;
iii. Energy consumed in intermodal transfer station (if any); and
iv. Energy consumed in Delivery leg of the task
Energy consumption in each of the above sections was modelled by segregating them
into the modes used. The review of literature showed that the contribution of energy
consumed in intermodal transfer process was less significant compared to other
section. Hence the energy consumed in this section was modelled based on aggregate
value reported in literature.
134
The study showed the energy efficiency between the modes varies considerably with
the alignment. Hence route alignment was given due consideration during energy
estimation. Another important factor that was investigated in the thesis was the
payload factor. The road sub-model was improved to reflect the payload contribution
on energy consumption. Based on simulation model result for trucks and the
literature reviewed, payload factor was determined to vary linearly between the
practical load carrying limits of heavy commercial vehicles.
Among the various energy influencing parameters, the parameters having a
prominent impact on freight corridor level study were considered. For some typical
base values, the influencing model parameters and its importance were determined
by the sensitivity analysis and the brief summary is shown in Table 8.1.
Importance order Rail sub-model Road sub-model 1 Grade Speed and Payload
2 Train Length Grade
3 Speed Congestion
4 Mass Curvature
5 Curvature Roughness
6 Number of axles
Table 8. 1 Importance of model parameters on road and rail fuel consumption
8.3 Case study
The developed model was applied to the existing and proposed freight corridors
crossing Toowoomba second range. The existing rail and road corridors were
compared to the proposed rail and road second range crossing on an energy
consumption basis. Based on the total fuel consumed to move a certain amount of
freight across the range, the determined fuel performances are shown in Table 8.2, in
the order of efficiency.
S.N. Mode and Corridor Efficiency (Lt./1000tonnes) 1 Train on Proposed Route 453.8 2 B-Double on Proposed Route 557.5 3 B-Double on Existing Route 586.3 4 Articulated Truck on Proposed Route 626.7 5 Articulated Truck on Existing Route 656.7 6 Train on Existing Route 807.5
Table 8. 2 Fuel Performance on proposed and existing corridors
135
8.4 Model application: on simulated cases
Various simulated cases were developed to illustrate the model application on
estimating door-to-door energy consumption. Pick-up and delivery component of
freight movement had a major impact on deciding which option is more energy
beneficial. When pick-up and delivery legs length consist of larger portion of the
total freight movement distance then the efficiency of the movement and the
advantage of intermodal freight movement were considerably reduced.
Type of train and road vehicle type was varied across the simulated cases so as to
illustrate the impact of vehicle properties on door-to-door energy performance. The
train properties of three train types (A,B and C) are given in Table 7.3 (Section 7.2).
Figure 8. 1 Performance of some simulated cases
An example of the results obtained is given in Figure 8.1, which shows that, Type C
train when combined with B-Double would provide the best freight moving option.
However, there is not much difference in efficiency when B-Double combined with
Type A train is compared against Articulated Truck combined with Type C train.
The simulated runs (presented in Chapter 7) also showed that fully loaded B-Double
in a road alone movement showed a better fuel performance than combination of
60% loaded Articulated Truck operating in conjunction with Type A train when road
136
pick-up leg and rail line-haul leg were equal in length. Hence, the performances of
both the components of freight movement are important and should be given a due
consideration while choosing the energy efficient freight moving option.
8.5 Future Research
The research has made numerous assumptions to simplify the estimation and
comparison process. The result presented here could be further improved with
sufficient data collection for validation purpose.
Future research in this field could focus towards reducing the measurement error and
increasing complexity of the model, but keeping the final computation relatively
simple for end users purposes. The increased complexity could be focused in
establishing a better relationship for the negative grade driving condition. Inclusion
of accelerating energy demand in the road and the rail sub-models, along with
braking energy consumption modelling, would improve the reliability of the model.
Future research could focus in including commodity type and interlinking them with
volume and weight that could be carried on different types of vehicles.
A limited class and speed range between 70 to 105 km/hr were used for determining
payload correction factor for the road sub-model. The model could be further
improved with in-depth study of payload correction factor and its variation across the
speed and vehicle class.
With those improvements in the model, it could be implemented on case study
corridor with more reliability. The accuracy could be further improved with
additional data on speed profile, congestion level and roughness on those study
corridors.
By adding other vehicle operating cost factors on both the sub-models, the developed
model and tool could be used as a decision making tool especially to plan a new
corridor and maintain or restructure the existing corridors.
APPENDICES
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Appendix A
Commodity Classification
Appendix A
1
Commodity classification *
Pack Classification Bulk Containerised Other freight
Type of Commodity Dry/Solid
Liquid (inc slurry or melted)
Gas (inc liquified gas) 6m 12m
Other length Unitised
Livestock (Uncrated)
Vehicles/ Crafts (Empty) Other
1 Food and Live Animals
Live animals
Meat and meat preparations
Dairy products and eggs
Fish, crustaceans and molluscs and preparations thereof
Cereals and cereal preparations
Fruit and vegetables; sugar cane
Sugar, sugar preparations and honey
Feeding stuff for animals (exc unmilled cereals)
Coffee, tea, cocoa, spices, margarine and miscellaneous edible products
2 Beverages and Tobacco
Beverages
Tobacco
3 Crude materials, inedible, except fuels
Hides, skins and furskins, raw
Oil seeds, oil nuts and oil kernels
Crude rubber (inc synthetics and reclaimed)
Wood, timber and cork
Pulp and waste paper
Textile fibres (other than wool tops) and their wastes (not manufactured into yarn or fabric)
Crude fertilizers and crude materials (exc coal, petroleum and precious stones)
Metalliferous ores and metal scrap
Crude animal and vegetable materials not elsewhere specified
Appendix A
Commodity Classification
Appendix A
2
Pack Classification Bulk Containerised Other freight
Type of Commodity Dry/Solid
Liquid (inc slurry or melted)
Gas (inc liquified gas) 6m 12m
Other length Unitised
Livestock (Uncrated)
Vehicles/ Crafts (Empty) Other
4 Mineral fuels, lubricants and related materials
Coal, coke and briquettes
Petroleum, petroleum products and related materials
Gases, natural and manufactured
5 Animal and vegetable oils, fats and waxes
Animal oils and fats
Fixed vegetable oils and fats
Animal and vegetable oils and fats, processed, and waxes of animal or vegetable origin
6 Chemical related products not elsewhere specified
Organic and inorganic chemicals
Dyeing, tanning and colouring materials
Medicinal and pharmaceutical products
Essential oils and perfume materials; toilet, polishing and cleansing preparations
Fertilizers, manufactured
Plastic materials, artificial resins and cellulose esters and ethers
Explosives and other chemical materials and products
Appendix A
Commodity Classification
Appendix A
3
Pack Classification Bulk Containerised Other freight
Type of Commodity Dry/Solid
Liquid (inc slurry or melted)
Gas (inc liquified gas) 6m 12m
Other length Unitised
Livestock (Uncrated)
Vehicles/ Crafts (Empty) Other
7 Manufactured goods classified chiefly by material
Leather, leather manufactures not elsewhere specified and dressed furskins
Rubber manufactures not elsewhere specified
Cork and wood manufactures (exc furniture)
Paper, paperboard and articles of paper pulp, of paper or of paperboard
Textile yarn, fabrics, made-up articles not elsewhere specified and related products
Non-metallic mineral manufactures not elsewhere specified
Iron and steel
Non-ferrous metals
Manufactures of metal not elsewhere specified
8 Machinery and transport equipment
Machinery, equipment, apparatus and appliances
Road vehicles and other transport equipment
9 Miscellaneous manufactured articles
Furniture and parts thereof
Articles of apparel and clothing accessories and footwear
Professional, scientific and controlling apparatus not elsewhere specified; photographic apparatus, equipment and supplies; optical goods not elsewhere specified; watches and clocks
Printed matter, plastic wares, toys and other miscellaneous manufactured articles
Appendix A
Commodity Classification
Appendix A
4
Pack Classification Bulk Containerised Other freight
Type of Commodity Dry/Solid
Liquid (inc slurry or melted)
Gas (inc liquified gas) 6m 12m
Other length Unitised
Livestock (Uncrated)
Vehicles/ Crafts (Empty) Other
10 Commodities and transactions not elsewhere specified
Mail and postal packages, not classified by commodity
Water
Special transactions and commodities not classified by kind
Animals, live not elsewhere specified
Armoured fighting vehicles, arms of war and ammunition therefore; parts of arms not elsewhere specified
Coins (other than gold coin) not being legal tender
Gold, non-monetary
Coins ( being legal tender); ships, boats and floating structures operating temporarily in Australian waters
Note:
These commodity classification links are in the early stage in the tool developed and need to further developed.
Appendix B
Representative vehicles and their characteristics
- 1 - Appendix B-
Table Representative vehicles and their characteristics
Basic fuel consumption equation coefficient
Vehicle Category
Maximum Mass GCM
(tonnes)
Effective Mass
GVM/GCM (tonnes)
Number of
Wheels
Fuel P = Petrol D = Diesel
Engine Power (kW)
Aerodynamic Drag (CD)
Frontal Area (Sq m)
A
B
C
1 Utility (2 axle 4 tyre) 2.5 4 P 100 0.6 2.2 59.9 1,915.30 0.0087
2 Light commercial van Petrol [P] P 59.9 1,915.30 0.0087
3 Light truck (2 axle 6 tyre) Petrol [P] 2.7 6 P 124 0.7 5.0 42.1 2,596.70 0.0234
4 Light truck (2 axle 6 tyre) Diesel [D] 4.2 6 D 90 0.7 5.0 42.0 1,948.00 0.0143
5 Medium truck (2 axle 6 tyre) 8 6 D 120 0.65 6.0 43.3 3,543.30 0.0159
6 Heavy Rigid Truck (3 axle) 65.1 5,408.30 0.0168
7 Rigid or Articulated 3 Axle Truck 14 10 D 170 0.6 8.0 65.1 5,408.30 0.0168
8 Articulated truck - 4 Axle 20 16 D 190 0.7 8.0 106.5 6,779.70 0.0169
9 Articulated Truck - 5 Axle 18 D 260 0.7 8.0 118.1 10,126.10 0.0158
10 Articulated Truck - 6 Axle 35 22 D 280 0.7 8.0 131.10 11,957.50 0.0148
11 Rigid (3 axle) + 5 Axle Dog Trailer 59.0 43 30 D 300 0.7 8.0 129.11 15,209.82 0.0180
12 Twinsteer + 4 Axle Dog Trailer 60.5 49 28 D 320 0.7 8.0 132.20 17,012.87 0.0180
13 Twinsteer + 5 Axle Dog Trailer 64.0 52 32 D 330 0.7 8.0 140.97 18,085.63 0.0190
14 B double Combination 45 30 D 320 0.8 8.0 172.70 14,720.40 0.0160
15 Road Train (double) 54 44 D 320 0.8 8.0 223.60 17,201.80 0.0148
16 A B Combination 99.5 74 54 D 350 0.8 8.2 254.94 23,765.82 0.0170
17 Road Train (triple) 85 64 D 360 0.8 8.2 312.10 26,646.90 0.0150
18 B Triple Combination 83.0 62 46 D 350 0.8 8.2 235.82 20,512.58 0.0180
19 Double B Double Combination 119.0 87 66 D 370 0.8 8.2 282.40 28,144.99 0.0170
Source: Thoresen (2003)
Appendix C
Gradient Adjustment Factors
1 Appendix C
Table Gradient Adjustment Factors
Speed (km/h) Vehicle
No.
Vehicle Type
Gradient Category 8 16 24 32 40 48 56 64 72 80 88 96 104
Utility 4% 0.04 0.11 0.10 0.10 0.11 0.13 0.14 0.16 0.13 0.10 0.09 0.05 0.02
(2 axles, 6% 0.06 0.20 0.16 0.17 0.19 0.21 0.24 0.28 0.24 0.20 0.16 0.11 0.07
4 tyres) 8% 0.08 0.34 0.32 0.33 0.35 0.39 0.44 0.49 0.44 0.37 0.27 0.21 0.15 1
10% 0.10 0.50 0.47 0.50 0.54 0.60 0.66 0.72 0.65 0.56 0.45 0.32 0.18
Light 4% 0.04 0.11 0.10 0.10 0.11 0.13 0.14 0.16 0.13 0.10 0.09 0.05 0.02
commercial 6% 0.06 0.20 0.16 0.17 0.19 0.21 0.24 0.28 0.24 0.20 0.16 0.11 0.07
van 8% 0.08 0.34 0.32 0.33 0.35 0.39 0.44 0.49 0.44 0.37 0.27 0.21 0.15 2
10% 0.10 0.50 0.47 0.50 0.54 0.60 0.66 0.72 0.65 0.56 0.45 0.32 0.18
Light truck 4% 0.02 0.04 0.04 0.03 0.02 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.02
(2 axles, 6% 0.03 0.07 0.07 0.06 0.06 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.03
6 tyres) 8% 0.06 0.13 0.15 0.16 0.16 0.13 0.06 0.06 0.05 0.05 0.05 0.05 0.05 3
Petrol [P] 10% 0.09 0.23 0.27 0.30 0.30 0.27 0.22 0.22 0.22 0.22 0.22 0.22 0.22
Light truck 4% 0.04 0.08 0.07 0.06 0.05 0.07 0.12 0.08 0.06 0.05 0.05 0.05 0.05
(2 axles, 6% 0.07 0.13 0.15 0.18 0.20 0.26 0.34 0.24 0.20 0.20 0.20 0.20 0.20
6 tyres) 8% 0.12 0.31 0.36 0.41 0.44 0.51 0.61 0.45 0.45 0.45 0.45 0.45 0.45 4
Diesel [D] 10% 0.21 0.50 0.58 0.64 0.67 0.77 0.86 0.86 0.86 0.86 0.86 0.86 0.86
Medium 4% 0.06 0.10 0.09 0.09 0.11 0.19 0.32 0.24 0.13 0.13 0.13 0.13 0.13
truck 6% 0.12 0.21 0.27 0.33 0.40 0.52 0.69 0.64 0.64 0.64 0.64 0.64 0.64
(2 axles, 8% 0.23 0.45 0.55 0.64 0.73 0.87 0.87 0.87 0.87 0.87 0.87 0.87 0.87 5
6 tyres) 10% 0.34 0.70 0.83 0.95 1.05 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12
Large truck 4% 0.09 0.12 0.11 0.13 0.20 0.28 0.43 0.46 0.42 0.42 0.42 0.42 0.42
(3 axles, 6% 0.16 0.26 0.33 0.44 0.55 0.69 0.76 0.76 0.76 0.76 0.76 0.76 0.76
10 tyres) 8% 0.29 0.53 0.65 0.79 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 6
10% 0.44 0.82 0.98 1.15 1.23 1.23 1.23 1.23 1.23 1.23 1.23 1.23 1.23
Articulated 4% 0.09 0.12 0.11 0.13 0.20 0.28 0.43 0.46 0.42 0.42 0.42 0.42 0.42
3 axle 6% 0.16 0.26 0.33 0.44 0.55 0.69 0.76 0.76 0.76 0.76 0.76 0.76 0.76
truck 8% 0.29 0.53 0.65 0.79 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 7
10% 0.44 0.82 0.98 1.15 1.23 1.23 1.23 1.23 1.23 1.23 1.23 1.23 1.23
Articulated 4% 0.12 0.14 0.13 0.16 0.24 0.32 0.44 0.44 0.44 0.44 0.44 0.44 0.44
4 axle 6% 0.20 0.28 0.38 0.50 0.61 0.67 0.67 0.67 0.67 0.67 0.67 0.67 0.67
truck 8% 0.36 0.59 0.73 0.86 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 8
10% 0.56 0.90 1.06 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14
Articulated 4% 0.01 0.14 0.13 0.19 0.28 0.38 0.47 0.47 0.47 0.47 0.47 0.47 0.47
5 axle 6% 0.25 0.29 0.40 0.53 0.66 0.73 0.73 0.73 0.73 0.73 0.73 0.73 0.73
truck 8% 0.45 0.60 0.75 0.89 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 9
10% 0.60 0.90 1.08 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17
Articulated 4% 0.06 0.14 0.14 0.21 0.31 0.42 0.48 0.48 0.48 0.48 0.48 0.48 0.48
6 axle 6% 0.09 0.29 0.41 0.54 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70
truck 8% 0.17 0.61 0.76 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 10
10% 0.25 0.91 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09
Large truck 4% 0.05 0.20 0.24 0.28 0.34 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41
(rigid 3 axle) 6% 0.10 0.30 0.41 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54
+ 5 axle 8% 0.19 0.61 0.75 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 11
dog trailer 10% 0.27 0.92 1.11 1.11 1.11 1.11 1.11 1.11 1.11 1.11 1.11 1.11 1.11
Twin steer 4% 0.05 0.20 0.25 0.29 0.35 0.44 0.44 0.44 0.44 0.44 0.44 0.44 0.44
truck + 6% 0.10 0.32 0.43 0.57 0.57 0.57 0.57 0.57 0.57 0.57 0.57 0.57 0.57
4 axle 8% 0.20 0.64 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 12
dog trailer 10% 0.29 0.97 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17
Twin steer 4% 0.05 0.20 0.25 0.29 0.35 0.44 0.44 0.44 0.44 0.44 0.44 0.44 0.44
truck + 6% 0.10 0.32 0.43 0.57 0.57 0.57 0.57 0.57 0.57 0.57 0.57 0.57 0.57
5 axle 8% 0.19 0.64 0.78 0.78 0.78 0.78 0.78 0.78 0.78 0.78 0.78 0.78 0.78 13
dog trailer 10% 0.29 0.97 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17 1.17
Appendix C
Gradient Adjustment Factors
2 Appendix C
Speed (km/h) Vehicle
No.
Vehicle Type
Gradient Category 8 16 24 32 40 48 56 64 72 80 88 96 104
B Double 4% 0.06 0.15 0.15 0.22 0.31 0.43 0.43 0.43 0.43 0.43 0.43 0.43 0.43
(tandem-tri) 6% 0.10 0.30 0.42 0.54 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63
8% 0.18 0.62 0.76 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 14
10% 0.27 0.93 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12
Road train 4% 0.07 0.16 0.15 0.19 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29
(double) 6% 0.11 0.29 0.39 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45
8% 0.21 0.61 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 15
10% 0.30 0.91 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.01
A B 4% 0.06 0.21 0.25 0.28 0.32 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39
Combination 6% 0.13 0.30 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40
8% 0.24 0.62 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 16
10% 0.35 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95
Road train 4% 0.16 0.17 0.13 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20
(triple) 6% 0.39 0.29 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34
8% 0.60 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 17
10% 0.75 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96
B Triple 4% 0.03 0.20 0.24 0.28 0.32 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37
6% 0.11 0.28 0.37 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52
8% 0.21 0.60 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.74 18
10% 0.31 0.89 1.11 1.11 1.11 1.11 1.11 1.11 1.11 1.11 1.11 1.11 1.11
Double B 4% 0.16 0.21 0.25 0.27 0.32 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40
Double 6% 0.40 0.30 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41
8% 0.62 0.62 0.62 0.62 0.62 0.62 0.62 0.62 0.62 0.62 0.62 0.62 0.62 19
10% 0.78 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96
Source: Thoresen (2003)
Appendix D
Roughness Adjustment Factors
1 Appendix D
Table Fuel Consumption Road Roughness Adjustment Factors (FCGRVF)
Speed (km/h) Stereotype Designation
Vehicle Stereotype 8 16 24 32 40 48 56 64 72 80 88 96 104
1 Utility (2 axles, 4 tyres) 0.03 0.07 0.08 0.08 0.09 0.10 0.11 0.11 0.09 0.09 0.09 0.09 0.07
2 Light commercial van 0.03 0.07 0.08 0.08 0.09 0.10 0.11 0.11 0.09 0.09 0.09 0.09 0.07
3 Light truck (2 axles, 6 tyres), Petrol [P]
0.03 0.06 0.07 0.08 0.08 0.08 0.07 0.07 0.07 0.06 0.06 0.05 0.04
4 Light truck (2 axles, 6 tyres), Diesel [D]
0.04 0.08 0.09 0.10 0.11 0.11 0.10 0.09 0.09 0.08 0.07 0.06 0.06
5 Medium truck (2 axles, 6 tyres)
0.05 0.09 0.10 0.11 0.12 0.14 0.14 0.12 0.11 0.11 0.10 0.08 0.08
6 Large truck (3 axles, 10 tyres)
0.05 0.09 0.10 0.11 0.12 0.14 0.17 0.14 0.13 0.12 0.12 0.10 0.09
7 Articulated 3 axle truck 0.05 0.09 0.10 0.11 0.12 0.14 0.17 0.14 0.13 0.12 0.12 0.10 0.09
8 Articulated 4 axle truck 0.06 0.10 0.11 0.13 0.14 0.16 0.18 0.19 0.16 0.15 0.13 0.12 0.11
9 Articulated 5 axle truck 0.00 0.09 0.11 0.12 0.14 0.15 0.17 0.19 0.20 0.17 0.16 0.15 0.13
10 Articulated 6 axle truck 0.04 0.10 0.12 0.13 0.15 0.17 0.18 0.20 0.20 0.19 0.19 0.17 0.16
11 Large truck (rigid 3 axle) + 5 axle dog trailer
0.05 0.11 0.12 0.14 0.16 0.18 0.20 0.21 0.22 0.23 0.20 0.19 0.18
12 Twin steer truck + 4 axle dog trailer
0.05 0.10 0.12 0.14 0.16 0.18 0.20 0.21 0.22 0.24 0.20 0.19 0.19
13 Twin steer truck + 5 axle dog trailer
0.05 0.11 0.12 0.14 0.16 0.18 0.20 0.22 0.22 0.24 0.21 0.20 0.19
14 B Double 0.05 0.10 0.12 0.14 0.16 0.17 0.19 0.20 0.20 0.22 0.19 0.18 0.17
15 Road train (double) 0.06 0.11 0.13 0.15 0.17 0.19 0.21 0.22 0.24 0.24 0.20 0.20 0.20
16 A B Combination 0.06 0.12 0.14 0.16 0.17 0.20 0.23 0.24 0.24 0.22 0.24 0.23 0.20
17 Road train (triple) 0.06 0.12 0.14 0.15 0.17 0.20 0.23 0.27 0.22 0.26 0.23 0.23 0.21
18 B Triple 0.06 0.12 0.14 0.16 0.18 0.20 0.23 0.24 0.24 0.23 0.23 0.22 0.19
19 Double B Double
Appendix E
Spreadsheet Tool Description and Users Guide
Appendix E
0
Appendix (Description of Spreadsheet Tool)
Appendix E
Spreadsheet Tool Description and Users Guide
Appendix E
1
Input Freight Characteristics Sheet
The Input Freight Characteristics sheet allows the user to define, and later identify, the
freight characteristics such as type of packing, size of freight and type of commodity.
These parameters are to be tallied at first so the user is better informed about the
number of containers required to carry the commodity and trips generated for the task.
The main aim of this sheet is to make an allowance for such judgement by informing
users about the available volume and freight volume. OMIT, a tool developed to
calculate the energy consumption and emissions for international freight transport to
and from Denmark, has also acknowledged the importance of volume in heavy vehicle
transport where the density of the load is less than 333 kg per m3 (IFEU 2002).
Australian Bureau of Statistic classifications, namely Australian Transport Freight
Commodity (ATFCC) and Australian pack classification (APC) have been adopted for
commodity and freight classification. The ATFCC classifies goods carried by type of
commodity while the Australian Pack Classification APC classifies cargo by its pack
characteristics, e.g. `in bulk' or `containerised'.
A code is to be entered in the identification code cell so as to later identify the
movement option/number. On the right of the code identification cell, there is a place
to enter the origin place of the freight and destination of the freight, such as Brisbane
and Adelaide.
Input Road Sheet
The Input Road sheet allows the user to input the freight movement characteristics of
the pickup, road line haul and delivery sections for each forward and backhaul
movement.
Backhaul movement will only be considered in the energy efficiency calculation if the
data are provided there. Otherwise the comparison would be based on forward
movement of the freight which means the tool does not assume full, half or empty
backhaul movement on its own.
Appendix E
Spreadsheet Tool Description and Users Guide
Appendix E
2
Option code
On top left hand corner of the sheet, there is a cell allotted for option code input. The
input parameters of this cell would be used to later identify the particular movement
among different options involved in the freight movement such as use of B double
instead of several semi trailers.
Section division
Both forward and backhaul movement of freight has been divided into three portions.
They are;
• Pickup (PU)
• Road Line Haul (RoLH)
• Delivery (De)
Figure E-1 Route division
The pickup section could be identified by abbreviation PU and similarly RoLH for
road line haul and De for delivery. In addition, when B accompanies those
abbreviations (such as B-PU, B-RoLH and B-De) then it is meant to denote backhaul
movement. Hence B-PU means pickup section for backhaul movement.
The pickup and delivery have the same type of movement nature. Hence the input
sections of pickup and delivery movements are similar.
Road line haul (RoLH) 01-15 Road line haul (RoLH) 30-45
De01-05 De11-15
PU01-05
PU06-10
PU11-15
De06-10
RoLH
16-30
Pick up
Delivery
Appendix E
Spreadsheet Tool Description and Users Guide
Appendix E
3
Each pickup and delivery movement is divided into 15 tables, each table corresponds
to a movement of a single pick up leg.
Table - Pickup Leg1/Delivery Leg 1 (PU01-PU05)
This is the first of the 15 tables to input operating characteristics of pickup legs. These
type of tables are also used here as an input frame for delivery leg’s details, and for
both forward and backward movement.
This single table is designed to accommodate operating characteristics of single
pickup/delivery leg. It would be possible to change the vehicle type even within a
single pickup/delivery leg for occasions where vehicles are changed even within one
pickup/delivery leg.
If there are 11 pickup legs then the user will input operating characteristics in 11
tables and leave the rest empty. Same is true in the case of delivery movement.
Table – Road line haul (RoLH01- RoLH15)
Road line haul movement has been divided into three sections to accommodate
maximum of three vehicle combination types comprising one fleet. Each section
(distinguished by writing ‘First/second/third of the 3 vehicles in the freight traffic
fleet’) is to accommodate the movement data of a single freight movement. The tool
only could accommodate three vehicles for one line haul freight movement.
Rows and columns of tables – Road line haul and Pickup/delivery
Rows
Each movement is to be divided into homogeneous operation based on similar
traffic and terrain characteristics. Each segregated movement is to be entered in a
single row of the spreadsheet. For example, if the vehicle travelled at a speed of 60
km/h for the whole trip length then also the trip is to be segregated based on the grade,
curvature and congestion condition of the road. These segregated segments are to be
input in a separate row.
Appendix E
Spreadsheet Tool Description and Users Guide
Appendix E
4
Columns
First column of the table (Road line haul, pickup and delivery) contains the unique ID
assigned to each segmented task, for example PU03 denotes the third portion of the
first pickup leg for forward movement and B-De09 denotes the forth portion of the
second delivery leg for backhaul movement. This ID number helps to later identify
the energy consumed in that particular section.
Second column of the table (Road line haul, pickup and delivery) holds a place to
choose a freight vehicle of that section. Whenever a mouse is pointed in those cells
there appears a list of vehicles. A number corresponding to the type of vehicle being
used is to be entered in the cells of second column.
Third column of the table (Road line haul, pickup and delivery) enable input of
specific energy consumption (MJ/net tonne-km) of that movement. It is recommended
to input the values in the cells (of third column) only in the case of high confidence in
specific energy consumption data (known in advance) of that particular section and
vehicle type. Whenever any values are input in these cells, the program overwrites the
calculated value with the data mentioned in the cells.
Fourth column of the table (Road line haul, pickup and delivery) contains cells to
input length (in km) of the travel segment. As discussed above, an entire pickup/line
haul/delivery travel is divided into homogeneous section. The user is to input the
length of each of such homogeneous section in different cells.
Fifth column of the table (Road line haul, pickup and delivery) holds a place to input
travel speed (in km/h) of that particular homogeneous section being considered in that
row.
Similarly, sixth, seventh, eighth, ninth and tenth columns of the table (Road line haul,
pickup and delivery) holds a place to input payload, volume to capacity ratio, grade
percent, curvature and roughness (NRM counts/km) respectively of that particular
homogeneous section being considered in that row.
Eleventh and twelfth columns enclose rooms to input starting point and ending point
of each homogeneous section. For example, if a vehicle is travelling a constant speed
from ABC to CDF and then from CDF to EFG, even though the vehicle maintains the
Appendix E
Spreadsheet Tool Description and Users Guide
Appendix E
5
same speed, but there is a rise in grade. In such case, the travel segment is divided into
two portions and ending point’s name (CDF in above example) of first portion will be
the same as starting portion of the second section portion.
Table – intramodal transfer
The table has four rows to accommodate four transfer processes in one way
movement (forward or backhaul). The freight transferring process (from one vehicle
to another) consumes energy as it involves lifting and stacking. The specific energy
consumption (MJ/kg or MJ/container) for these processes are open for user input. In
the case where the users are not aware of the value, the tool uses default values. The
spreadsheet tool gives priority to the MJ/Container value for the estimation of energy
consumption in transfer process.
The first column of the table (intramodal transfer) is to contain the ID of two sections.
These two are the sections between which the transfer process occurs/occurred. For
example, if there is a transfer of freight from pickup section (PU15) directly into road
line haul section (RoLH01), then the first column should contain PU15 – RoLH01.
The second column of the table (intramodal transfer) is to contain the exact name of
the transfer location, such as the Port of Brisbane.
The third and forth columns of the table (intramodal transfer) is to contain the mass
involved in the transfer process and container involved in the transfer process
respectively.
The fifth and sixth columns of the table are open for users if they opt to overwrite the
default freight transfer specific value in MJ/kg and MJ/container unit respectively.
Input Rail Sheet
Rail line haul movement is expected to be accompanied by road legs as discussed
previously. The input framework of the road movement segment in Input Rail Sheet is
same as in pickup and delivery section of Input Road Sheet.
Appendix E
Spreadsheet Tool Description and Users Guide
Appendix E
6
However, in the input rail sheet users can only input the operating characteristics of
three pick up/delivery vehicles at once. The input room to enter operating
characteristics of each pick up/delivery vehicle is separated by variation in colour.
The table following the input pickup table is input table for intermodal transfer. This
intermodal transfer table is similar to the intermodal transfer table discussed earlier in
case of road transport. Hence, the readers are directed to above section for more detail
information about intermodal transfer table. However, unique to the rail operating
characteristics, there is a room to enter the shunting energy demand also. The cell
allotted for this purpose is few rows below the room allotted to input intermodal
transfer detail.
Rail line Haul Table
The rail line haul table has 140 rows. Each row is to be separated by the change in
operating characteristics to the train. These operating characteristics of the train are to
be input in the same table, ranging from column 2 to column 5. The first column
contains a unique ID assign to each rail line haul movement. These assigned ID are
not for users to change. They would help users to later identify the freight moving
section. Second column contains space to input train speed, third and fourth column
contains room to input route characteristics such as grade and curvature. There are
hints provided for proper input of grade and curvature value. Fifth column contains
the space allotted for input of distance value between the points of whose operating
characteristics are entered in that row.
The other adjoining table, at the right side of the rail line haul operating characteristics
table, is the input table to enter physical properties of rail. It contains the space for
input of train length, efficiency, number of wagons, etc. These are the parameters
assumed to remain same for the entire freight movement under consideration.
This rail line haul table is followed by intermodal transfer table, which is already
discussed above. This intermodal transfer table is followed by delivery leg table. The
delivery leg input table is similar to pick up leg input table.
Appendix E
Spreadsheet Tool Description and Users Guide
Appendix E
7
Vehicle Characteristics Sheet
A set of representative vehicles for road were chosen. The characteristics include
vehicle mass, drag area and friction area. For any type of unique vehicle set not
included in vehicle characteristics sheet the default value may not give a good
estimate of fuel consumption. In such cases the default values could be overwritten by
user specified value, provided the user have a good set of data describing the fuel
consumption of the chosen vehicle set. Those data are to be used in input sheets rather
than vehicle characteristics sheet.
Lookup tables Sheet
Lookup table sheet contains the information needed to quantify the effect of
adjustment factors such as curvature, grade, engine efficiency, roughness and
congestion on road fuel consumption. The corresponding data from these tables will
be selected to aid in computing fuel consumption.
Calculation
In calculation sheet, the data from input sheets are used and computed along with data
from the lookup table sheet. The sheet contains the necessary instruction to match the
input data and data from lookup table. After extracting the information from all the
relevant sheets, fuel consumption for the specified section is computed in the
calculation sheet and sent to output sheets. Generally users are not to alter the settings
and formula of this sheet.
Output Road Sheet
Output Road Sheet accepts the data from corresponding Input Sheets and Calculation
Sheet and display the amount of fuel consumed on each trip segment. The sheet also
tabulates the parameters considered for estimating energy consumption and their
relative impact ratio.
The Output Road Sheet uses the similar format of Input Road Sheet. The Output Road
Sheet portrays the fuel consumption figure of each divided route section and energy
consumed in transfer process.
Appendix E
Spreadsheet Tool Description and Users Guide
Appendix E
8
The first column contains the ID number of travel segment which is same as in Input
Road Sheet. The second column shows type of vehicle as per the number selected in
Input Road Sheet. Column number three to fifteen shows the different parameters
being considered during fuel consumption estimation and their relative magnitude.
Column sixteen contains the estimated value of fuel consumed during that particular
travelling (for each homogeneous section distinguished by different rows).
Output Rail Sheet
There is a pickup and delivery leg’s fuel consumption description which is expected
to be accompanied by road. Hence these sections of Output Rail Sheet are similar to
that of Output Road Sheet.
The energy performance for the set of operating and train characteristics input in
‘Input Rail Sheet’ is presented in Rail Line Hail Output Table. The performance of
values entered in each row (representing an each segment on the ground) could be
identified based on the unique ID (such as RaLH10) and start and end point
description made in ‘Input Rail Sheet’.
A separate table portrays estimated energy consumption for the transferring of freight
between two modes.
Summary Sheet
Summary sheet accepts the energy consumption value estimated in calculation sheet
and presented in corresponding Output Sheets and makes the comparison between the
options provided (two options at a time, involving road/rail and road). A separate
column in the Summary Sheet portrays the effect of full fuel cycle consideration in
comparison differentiating the diesel and electricity powered freight movement.
The terms in summary sheet are self explanatory and all the values shown are based
on the estimated values and user input values. The users are not to enter any values
and change the settings of this sheet.
NOTE:
Not all the subroutine of this spreadsheet has been fully developed.
Appendix F
Spreadsheet Tool – A CD
Appendix F
1
Appendix G
Vehicle Simulator Result (A Sample) after processing
Appendix G 1
Payload
GVM 72 80 89 97 105 113
1814 156.7398 180.1802 208.3333 238.6635 271.0027 306.7485
1900 156.9859 180.5054 208.7683 239.2344 271.7391 306.7485
1950 157.2327 180.8318 208.7683 239.2344 271.7391 307.6923 02000 157.4803 181.1594 209.205 239.2344 271.7391 307.6923 0.000819
2100 157.9779 181.4882 209.6436 239.8082 272.4796 307.6923 0.002457
2200 158.2278 181.8182 210.084 240.3846 272.4796 308.642 0.0040952300 158.7302 182.1494 210.084 240.9639 273.224 308.642 0.005733
2500 159.4896 183.1502 210.9705 241.5459 273.9726 309.5975 0.0090092700 160.2564 183.8235 211.8644 242.1308 274.7253 310.559 0.012285
3000 161.5509 185.1852 213.2196 243.309 276.2431 311.5265 0.0171993500 163.6661 187.2659 215.0538 245.7002 277.7778 313.4796 0.025389
4000 165.5629 189.0359 217.3913 247.5248 280.112 315.4574 0.033579
4500 167.5042 191.2046 219.2982 249.3766 282.4859 317.4603 0.0417695000 169.7793 193.4236 221.2389 251.8892 284.0909 319.4888 0.049959
5500 171.5266 195.3125 223.2143 253.8071 286.533 321.5434 0.0581496000 173.6111 197.2387 225.7336 255.7545 288.1844 323.6246 0.066339
6500 175.7469 199.2032 227.7904 257.732 289.8551 325.7329 0.0745297000 177.6199 201.2072 229.3578 259.7403 292.3977 327.8689 0.082719
8000 181.8182 205.3388 233.6449 263.8522 295.858 332.2259 0.099099
9000 185.8736 209.6436 238.0952 268.0965 300.3003 335.5705 0.11547910000 190.1141 213.6752 242.1308 271.7391 303.9514 340.1361 0.131859
11000 194.1748 217.8649 246.3054 276.2431 308.642 343.6426 0.14823912000 198.0198 221.7295 250 280.112 312.5 348.4321 0.164619
13000 202.0202 225.7336 254.4529 284.0909 316.4557 352.1127 0.18099914000 206.1856 229.8851 258.3979 288.1844 320.5128 355.8719 0.197379
15000 210.084 234.192 262.4672 292.3977 324.6753 359.7122 0.213759
16000 214.1328 238.0952 266.6667 296.7359 328.9474 363.6364 0.23013917000 218.3406 242.1308 271.0027 300.3003 332.2259 367.6471 0.246519
18000 222.2222 246.3054 275.4821 304.878 336.7003 371.7472 0.26289919000 226.2443 250 279.3296 308.642 341.2969 375.9398 0.279279
20000 229.8851 254.4529 283.2861 313.4796 344.8276 380.2281 0.29565921000 234.192 258.3979 287.3563 317.4603 348.4321 384.6154 0.312039
22000 238.0952 262.4672 291.5452 321.5434 353.3569 389.1051 0.328419
23000 242.1308 266.6667 295.858 325.7329 357.1429 393.7008 0.34479924000 246.3054 271.0027 300.3003 330.033 361.0108 396.8254 0.361179
25000 250 274.7253 303.9514 333.3333 364.9635 401.6064 0.37755926000 253.8071 278.5515 308.642 337.8378 369.0037 404.8583 0.393939
27000 258.3979 283.2861 312.5 342.4658 373.1343 409.8361 0.41031928000 261.7801 287.3563 316.4557 346.0208 377.3585 413.2231 0.426699
29000 265.9574 291.5452 321.5434 349.6503 381.6794 418.41 0.443079
30000 270.2703 294.9853 325.7329 354.6099 386.1004 421.9409 0.45945931000 273.9726 299.4012 330.033 358.4229 389.1051 425.5319 0.475839
32000 277.7778 303.9514 333.3333 362.3188 393.7008 431.0345 0.49221933000 281.6901 307.6923 337.8378 366.3004 398.4064 434.7826 0.5086
34000 285.7143 311.5265 342.4658 370.3704 401.6064 438.5965 0.5249835000 289.8551 316.4557 346.0208 374.5318 406.5041 442.4779 0.54136
36000 294.1176 320.5128 350.8772 378.7879 409.8361 448.4305 0.55774
37000 298.5075 324.6753 354.6099 383.1418 414.9378 452.4887 0.5741238000 302.1148 327.8689 359.7122 387.5969 418.41 456.621 0.5905
39000 305.8104 332.2259 363.6364 392.1569 423.7288 460.8295 0.6068840000 310.559 336.7003 367.6471 396.8254 427.3504 465.1163 0.62326
41000 314.4654 340.1361 371.7472 400 431.0345 469.4836 0.63964
42000 318.4713 344.8276 375.9398 404.8583 434.7826 473.9336 0.6560243000 322.5806 348.4321 380.2281 408.1633 440.5286 478.4689 0.6724
Speed (km/h)
Less than tare weight
Appendix G
A Screen Capture of Vehicle Run Simulator (Design Pro)
Appendix G 2
Figure G-1 Screen Capture of Design Pro Vehicle Simulator
Appendix H
Toowoomba Case Study: Proposed Road Alignment Details
Appendix H
1
Curvature and Grade details of proposed road alignment extracted from Maunsell (1998) Chainage Horizontal curve Grade
(m) (Radius in m) (percentage)
0-500 1220 0500-750 1500 0
750-1000 650 0
1000-1700 650 1.671700-2000 St Section 1.67
2000-2586 St Section -0.85
2586-2860 St Section 2.5
2860-3500 1900 2.53500-4164 1900 0.83
4164-4500 St Section 3.46
4500-5000 2200 4.965000-5357 2200 4.96
5357-5500 2200 0
5500-5857 2200 -1.795857-7000 1000 2.95
7000-8000 1000 -4
8000-8186 St Section 0
8186-8500 650 5.58500-9500 750 5.5
9500-10000 1500 5.5
10000-10500 1000 3.4510500-10857 1000 3.45
10857-11357 1050 3.45
11357-12000 660 3.45
12000-12500 660 2.0712500-13000 St Section 0.82
13000-13500 1220 -0.43
13500-14000 1500 -1.514000-14286 1500 -1.5
14286-14357 1500 0
14357-15315 1500 5.515315-15500 St Section 5.5
15500-16500 900 5.5
16500-17500 600 5.5
17500-17793 610 5.517793-18000 610 2.14
18000-18715 610 -4.6
18715-19265 St Section -4.619265-19500 St Section 0
19500-19886 St Section 1.88
19886-20000 St Section 0
20000-20379 3000 -1.420379-21000 3000 2
21000-21700 St Section 2
21700-22000 St Section 1.822000-23000 1200 -5.15
23000-23250 1200 0
23250-23500 St Section 0
23500-24000 1000 3.6324000-24500 1000 0
24500-25000 St Section -2.64
25000-25500 St Section 0.7825500-26000 St Section -2.1
26000-26500 1000 -2.1
26500-27500 1000 0.627500-27850 St Section 0.6
27850-28000 St Section 0
28000-28500 St Section -0.89
Appendix I
Toowoomba Case Study: Existing Road Alignment Details
Appendix I
1
Existing Road alignment details extracted from maps provided by DMR, Toowoomba.
EAST Bound
Toowoomba to Dalby
Chainage (m) Grade Horizontal curve (m)
3562-3624 -2 930 Starts from Nugents Pinch Road3624-3725 -0.55 930 (moving away from Toowoomba)
3725-3850 0.39 9303850-3900 0 930
3900-4120 0
4120-4220 -1.28
4220-4350 -1.964350-4393 -2.96 0
4393-4475 -2.96 913.254475-4597 -3.3 913.254597-4700 -3.3 0
4700-4800 -3.43 04800-4925 -3.38 0
4925-5050 -3.07 05050-5114 -1.72 0 Gowrie Junction, Charlton Road
5114-5275 -1.33 05275-5350 -1.6 0
5350-5363 -2.44 05363-5400 -2.44 2988.255400-5500 -2.74 2988.25
5500-5595 -3.77 2988.255595-5700 -4.74 0
5700-5775 -5.44 05775-5850 -6.16 0
5850-5900 -6.6 05900-5985 -6.88 0
5985-6050 -6.62 06050-6130 -5.83 06130-6220 -5.22 0 This section grade is not clear in the plan
6220-6350 -4.47 06350-6515 -2.95 0
6515-6750 -1.76 06750-6888 -1.13 0
6888-7100 -0.4 10007100-7163 -0.8 1000
7163-7225 -1.22 07225-7313 -1.81 0
7313-7463 -2.31 07463-7550 -1.1 07550-7700 -0.5 0
7700-7740 -1.3 07740-7800 -1.3 1000
7800-7850 -1.94 1000 155315 Joins Nass Road and Wirths Road
Plan No. 155314
Plan No. 155308
Plan No. 155309
Plan No.
256615
Plan No. 155305
Plan No.
155307
Plan No.
155311
Plan No. 171634
Plan No. 155310
WEST Bound
Ipswich to Toowoomba
Chainage (Ft) Grade Horizontal curve (Ft)
77100-77500 1.45 5973 Starts from Paynter Road
77500-78400 0 5973
78400-78946 1.56 597378946-80060 1.56 0
80060-81200 1.56 5027
81200-81800 0.32 5027
81800-82034 -0.36 502782034-83000 -0.92 0
83000-83200 0 0
83200-83538 1.38 083538-84200 3.22 3000
84200-84800 1.52 3000
84800-85854 0.55 3000 Connoles Road junction85854-86200 0.55 0
86200-86800 -0.59 0
86800-87000 0.01 0
87000-88400 1.93 0 Murphys Creek Road junction88400-89200 1.12 0
89200-90200 4.31 0 Blanchview Road junction90200-90900 2.02 0
90900-91400 0 0
91400-91900 0.68 0 Park Ridge Road junction91900-92000 0 0
92000-92200 0.94 092200-93000 3.51 0
28400-28450 2 028450-28730 2.45 0
28730-28825 2.84 0
28825-28863 2.33 0
28863-28975 2.33 100028975-29150 2.33 0
without refering to the suggested drawing
29150-29473 2.48 0
29473-29750 2.1 Roches Road junction29750-29900 2.1
29900-30075 3.530075-30250 2.08
30250-30500 2.07 Plan
30500-30775 4.19 18034730775-30915 1.5 500 Plan
30915-31115 1.5 550 325307
31115-31375 8 550
31375-31525 8.4 031525-31665 8.4 2000
31665-31715 8.4 0
31715-31761 8.7 031761-31863 8 360
31863-32000 8 0
32000-32131 8 304.832131-32300 8 0
32300-32400 7.25 15240
32400-32525 6.58 0
32525-32550 6.58 38132550-32650 9.8 381
32650-32885 9.8 0
32885-33010 9.8 19833010-33060 9.8 0
33060-33150 9.8 152.4
33150-33200 9.8 0
33200-33250 10.17 0
33250-33363 10.17 198
33363-33430 10.17 033430-33450 10.17 129.54
33450-33650 9.9 129.54
33650-33687 9.06 129.54
33687-33835 9.06 033835-34000 9.06 121.92
34000-34050 9.06 0
34050-34195 8.87 198.12
34195-34350 8.87 0
34350-34425 8.29 0
34425-34600 8.29 121.9234600-34663 8.29 381
34663-34713 8.29 0
34713-34875 8.29 120 Ends at East Street
Drawing 325308
Drawing
180348
DMR Toowoomba Maps
Measurement in meter
Drawing No. 325118
Plan No. 106833
Plan No.
106834
Plan No.
106835
Plan No.
106836
Plan No. 180522
Plan No.
180352
Drawing
180349
Drawing
180350
Appendix J
Toowoomba Case Study: Existing Rail Alignment Details
Appendix J
1
Existing track alignment details extracted from Western System Information Pack, QR 2001
Chainage (km) Grade (1in X) Horizontal curve (m)
131.34-132.00 103 0
132.00-132.13 0 0
132.13-132.31 205 201132.31-132.39 0 0
132.39-132.68 54 0
132.68-133.00 50 181
133.00-133.07 110 0
133.07-133.21 110 201133.21-133.29 110 0
133.29-133.55 50 201
133.55-133.79 57 160
133.79-133.87 57 0
133.87-133.95 57 241
133.95-134.16 57 0134.16-134.26 57 140
134.26-134.47 50 140
134.47-134.74 55 120
134.74-134.87 51 120
134.87-135.25 51 0
135.25-135.42 0 0135.42-135.89 50 181
135.89-136.00 64 0
136.00-136.32 64 110
136.32-136.37 64 0
136.37-136.42 64 140
136.42-136.68 64 120136.68-136.74 67 0
136.74-137.00 67 100
137.00-137.13 67 130
137.13-137.31 50 0
Chainage (km) Grade (1in X) Horizontal curve (m)
137.31-137.37 50 201
137.37-137.76 50 150
137.76-138.00 50 140
138.00-138.13 50 130
138.13-138.24 51 100
138.24-138.63 51 140138.63-138.71 51 0
138.71-139.00 51 160
139.00-139.11 64 228
139.11-139.26 64 101
139.26-139.32 64 201
139.32-139.42 70 0139.42-139.53 70 140
139.53-139.63 69 100
139.63-139.71 61 100
139.71-139.87 64 100
139.87-140.00 54 123
140.00-140.13 51 0140.13-140.27 51 301
140.27-140.39 51 0
140.39-140.53 102 100
140.53-140.63 68 261
140.63-140.68 68 0
140.68-140.76 68 100140.76-140.95 59 100
140.95-141.05 59 0
141.05-141.13 59 402
141.13-141.16 59 0
141.16-141.29 59 150
141.29-141.64 110 100141.64-141.79 110 140
141.79-141.92 110 120
141.92-141.97 106 120
Chainage (km) Grade (1in X) Horizontal curve (m)
141.97-142.04 106 201
142.04-142.18 55 0
142.18-142.25 76 100142.25-142.61 76 140
142.61-142.74 56 0
142.74-142.92 56 110142.92-143.03 114 100
143.03-143.13 114 110
143.13-143.18 114 0143.18-143.26 71 130
143.26-143.39 71 110
143.39-143.55 71 100143.55-143.63 52 100
143.63-143.74 52 201
143.74-143.87 52 0143.87-143.94 75 241
143.94-144-.00 75 0
144.00-144.11 75 120144.11-144.42 75 100
144.42-144.53 71 110
144.53-144.63 71 0144.63-144.68 71 201
144.68-144.76 71 0
144.76-144.82 71 120144-82-144.89 71 0
144-89-145.00 71 120
145.00-145.22 78 0145.22-145.35 78 402
145.35-145.41 78 0145.41-145.47 78 301
145.47-145.58 78 160
145.58-145.74 78 120145.74-145.82 78 102
145.82-145.89 52 0
145.89-145.95 52 100145.95-146.00 52 100
146.00-146.11 210 100
146.11-146.31 210 160146.31-146.39 53 0
146.39-146.45 53 402
146.45-146.63 63 160146.63-146.73 50 0
146.73-146.80 50 301
146.80-147.05 50 191147.05-147.16 50 140
147.16-147.29 60 0
146.29-147.58 60 241147.58-147.89 880 100
147.89-148.00 55 100
148.00-148.11 55 160148.11-148.21 55 0
148.21-148.42 300 100
148.42-148.58 300 160148.58-148.63 55 160
148.63-148.68 55 201
148.68-148.79 55 0148.79-148.84 110 100
148.84-148.89 110 0
148.89-149.00 225 201149.00-149.05 225 0
149.05-149.26 58 160
149.26-149.37 58 201149.37-149.50 58 100
149.50-149.76 50 0
149.76-149.82 83 0149.82-149.92 83 301
Appendix J
Toowoomba Case Study: Existing Rail Alignment Details
Appendix J
2
Existing track alignment details extracted from Western System Information Pack, QR 2001
Chainage (km) Grade (1in X) Horizontal curve (m)
149.92-150.00 165 100
150.00-150.16 60 100150.16-150.26 50 0
150.26-150.37 50 140150.37-150.45 88 0
150.45-150.55 88 100150.55-150.71 88 0
150.71-150.80 60 0150.80-150.92 60 201
150.92-151.37 60 100151.37-151.42 528 120
151.42-151.53 528 0151.53-151.68 528 120
151.68-151.82 264 0151.82-152.08 264 140
152.08-152.16 0 100152.16-152.26 0 0
152.26-152.32 0 110152.32-152.38 106 110
152.38-152.55 106 150152.55-152.63 106 0
152.63-152.74 106 160152.74-152.79 106 0
152.79-152.92 106 110152.92-153.00 60 120
153.00-153.05 60 0153.05-153.16 60 301
153.16-153.26 60 0153.26-153.32 60 100
153.32-153.47 86 100153.47-153.61 86 0
153.61-153.79 86 100153.79-153.95 86 0
153.95-154.00 86 100154.00-154.13 86 341
154.13-154.36 99 100154.36-154.52 106 100
154.52-154.58 106 0154.58-154.76 106 100
154.76-154.89 106 0154.89-155.00 104 150
155.00-155.03 104 0155.03-155.37 104 100
155.37-155.52 77 100155.52-155.63 77 402
155.63-155.71 77 0155.71-155.79 77 220
155.79-155.92 50 145155.92-156.13 50 0
156.13-156.18 50 208156.18-156.26 0 0
156.26-156.37 0 104156.37-156.42 0 0
156.42-156.53 60 301156.53-156.63 60 130
156.63-156.68 60 0156.68-156.76 60 160
156.76-156.84 55 160156.84-156.95 55 0
156.95-157.05 66 100157.05-157.13 50 0
157.13-157.29 50 140157.29-157.34 75 0
157.34-157.58 75 160157.58-157.82 75 0
157.82-157.87 84 0157.87-158.12 84 442
Chainage (km) Grade (1in X) Horizontal curve (m)
158.12-158.36 120 0
158.36-158.52 125 402158.52-158.73 125 1207
158.73-159.03 125 0
159.03-159.25 110 0
159.25-159.38 110 402
159.38-159.45 90 402
159.45-159.55 90 0
159.55-159.79 90 402
159.79-159.88 90 0
159.88-160.08 90 754160.08-160.16 90 0
160.16-160.32 90 301
160.32-160.47 0 0
160.47-160.58 0 221
160.58-160.74 0 0
160.74-160.86 0 140
160.86-161.06 0 0
161.06-161.16 0 221
161.16-161.37 0 0 Toowoomba (586)
000.00-000.20 0 0
000.20-000.30 0 221
000.30-000.51 0 0
000.51-000.57 2200 221
000.57-000.64 2200 100
000.64-000.73 102 100
000.73-000.84 79 0
000.84-000.93 79 354
000.93-001.00 146 0
001.00-001.24 146 804001.24-001.42 146 0
001.42-001.74 100 804
001.74-001.89 100 0
001.89-002.00 100 402
002.00-002.18 0 402
002.18-002.46 142 0
002.46-002.62 142 804
002.62-002.88 142 0
002.88-003.15 127 804
003.15-003.42 127 0003.42-003.58 127 1207
003.58-003.74 127 0
003.74-003.87 102 0
003.87-004.05 102 301
004.05-004.26 102 201
004.26-004.52 102 0
004.52-004.63 128 0
004.63-004.88 128 241
004.88-004.95 89 241004.95-005.47 89 301
005.47-005.68 89 0
005.68-005.74 89 241
005.74-005.89 99 241
005.89-006.00 99 0
006.00-006.52 93 201
006.52-006.63 93 281
006.63-006.76 97 281
006.76-006.89 97 0
006.89-007.05 97 301007.05-007.26 96 0
007.26-007.63 126 0
007.63-007.92 108 0
007.92-008.18 90 301
008.18-008.42 90 0
008.42-008.50 90 804
008.50-008.64 96 804
008.64-008.74 96 0
008.74-009.13 96 352
009.13-009.33 108 0009.33-009.42 108 1609
009.42-009.52 108 0
009.52-009.88 88 0
009.88-010.16 88 804
010.16-010.32 88 0
010.32-010.74 139 0
010.74-010.95 93 402
010.95-011.52 93 0
011.52-011.65 150 0011.65-011.84 220 603
011.84-012.00 220 0 Gowrie Junction
Appendix K
Toowoomba Case Study: Proposed Rail Alignment Details
Appendix K
1
Proposed Track Alignment Details extracted from QR’s corridor study.
110800 0 162.183 1.566111000 200 165.315 1.566
111000 200 165.315 1.565714111176.98 376.98 168.086 1.565714111176.98 376.98 168.086 1.563858
111200 400 168.446 1.563858111200 400 168.446 1.607534
111336.98 536.98 170.648 1.607534111336.98 536.98 170.648 1.657291
111689.302 889.302 176.487 1.657291111689.302 889.302 176.487 1.635113344.776 2544.776 203.554 1.635113344.776 2544.776 203.554 1.656274113981.446 3181.446 214.099 1.656274113981.446 3181.446 214.099 1.625056115100.792 4300.792 232.289 1.625056115100.792 4300.792 232.289 1.657013115881.293 5081.293 245.222 1.657013115881.293 5081.293 245.222 1.614961117082.003 6282.003 264.613 1.614961117082.003 6282.003 264.613 1.66144118468.932 7668.932 287.656 1.66144118468.932 7668.932 287.656 1.635181118710.312 7910.312 291.603 1.635181118710.312 7910.312 291.603 1.659488119687.059 8887.059 307.812 1.659488119687.059 8887.059 307.812 1.624717120139.938 9339.938 315.17 1.624717
120139.938 9339.938 315.17 1.652912120574.686 9774.686 322.356 1.652912120574.686 9774.686 322.356 1.625011
120881.33 10081.33 327.339 1.625011120881.33 10081.33 327.339 1.64943
121307.356 10507.36 334.366 1.64943121307.356 10507.36 334.366 1.624774121717.813 10917.81 341.035 1.624774121717.813 10917.81 341.035 1.650476122159.322 11359.32 348.322 1.650476122159.322 11359.32 348.322 1.624956122997.314 12197.31 361.939 1.624956122997.314 12197.31 361.939 1.654189123519.564 12719.56 370.578 1.654189123519.564 12719.56 370.578 1.625036124395.483 13595.48 384.812 1.625036124395.483 13595.48 384.812 1.66397129755.736 18955.74 474.005 1.66397129755.736 18955.74 474.005 1.635007130526.375 19726.38 486.605 1.635007130526.375 19726.38 486.605 1.655552
130890 20090 492.625 1.655552
Chainage from drawing
Chainage Distance
200
176.98
1655.474
636.67
Grade (%)
1119.346
780.501
1200.71
1386.929
241.38
976.747
452.879
434.748
306.644
426.026
410.457
441.509
770.639
363.625
RL
23.02
136.98
352.322
837.992
522.25
875.919
5360.253
Curvature
2204
2204
0
0
0
2200
0
1704
0
1205
0
2204
0
1704
0
1700
0
1704
0
2204
0
1700
0
1704
0
(Only the data for section under consideration here are presented)
Appendix L
Route Alignment Detail of Simulated Cases
Appendix L 1
0
1
2
3
Route Alignment Properties
Perc
en
tag
e (
%)
1% Grade + Curvy
2% Grade + Less Curvy
3% Grade + Very Curvy
4% Grade + Curvy
5% Grade + Almost Straight
1% Grade
2% Grade
3% Grade
5% Grade
6% Grade
7% Grade
Less Curvy
Almost Straight
Curvy
Very Curvy
Figure L1 Road Line Haul Route Alignment
0
1
2
3
Pe
rcen
tag
e (%
)
0.25% Grade + 700 m Radius Curve
0.5% Grade + 500 m Radius Curve
0.75% Grade + 600 m Radius Curve1% Grade + 1000 m Radius Curve
1.5% Grade + 2000 m Radius Curve
0.25% Grade
0.30% Grade
0.40% Grade
0.50% Grade0.75% Grade
1% Grade
1.5% Grade
2000 m Radius Curve
1000 m Radius Curve
700 m Radius Curve500 m Radius Curve
400 m Radius Curve
300 m Radius Curve
Figure L2 Rail Line Haul Route Alignment
0
1
2
3
4
Route Alignment Properties
Perc
en
tag
e (
%)
1% Grade + Less Curvy
2% Grade + Curvy
3% Grade + Less Curvy
5% Grade + Almost Straight
1% Grade + Very Curvy
Very Curvy
Curvy
Less Curvy
Almost Straight
8% Grade
6% Grade
5% Grade
4% Grade
3% Grade
2% Grade
Figure L3 Road Pickup and Delivery leg Route Alignment
