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1. Introduction and Background• Driver performance can be influenced by surrounding vehicle. “It is well known that
the surrounding roads and traffic environment influences driver’s behaviour; forexample, the road environment (surrounding landscape, road characteristics), trafficcomposition (cars and heavy vehicles) affects driver’s desired speed, lane changingbehaviour, lateral positioning, and overtaking behaviour” (Antonson, H., 2009;Olstam, J. 2009; Moridpour, S et al., 2010).
• There is also substantial research about other influencing factors such as distraction,fatigue, and personality on driving performance, but could something as simple as thelane position of another vehicle influence your performance.
1.1. Aims & ObjectivesAIM: To investigate the interaction between surrounding driver behaviours and drivingcontrols.
OBJECTIVES: are to determine:
1. The extent to which a lead driver’s behaviour influences driving performance andvehicle control of a following driver on (Rural roads)2. Which lead vehicle type has greater influence on drivers’ performance and vehiclecontrol? (Car vs HGV)3. Who is likely to be more affected by lead vehicle aggressive driving behaviour? (Maledriver vs female driver)
4. Research Methodology
Simulator Validity• Ideally this study will require the simulator validity to be closely related to real
world driving in order to consider the simulator as an adequate tool.• Selection of simulator is based on trade-off between (validity and controllability)
Participant Sample• Findings show that young drivers aged 17-25 are particularly prone to have
relatively more accidents than other driver (Clarke, D et al., 2006). Thecharacteristics of young driver accidents includes: accidents on single carriagewayrural roads; loss of control; excess speeding; accident during darkness (Clarke, D etal., 2006).
• Male drivers have more accidents compared to their female counterpart (Clarke, Det al., 2006; Jiménez-Mejías, E et al., 2014).
• 20 young drivers (10 males and 10 females) will be recruited for this study. Thissample size was informed by a similar driving simulator study on the comparison ofdriving styles (Pampel, S. M., et al., 2015).
3. Literature ReviewThe idea behind this study is connected to earlier road safety paradigm and researchcarried out between 1950 and 1970 which tried to establish the cause of accidentsas being “Road user, or the vehicle, or the road” (Hagenzieker, M.P et al., 2014).
References Antonson, H., Mårdh, S., Wiklund, M., & Blomqvist, G. (2009). Effect of surrounding landscape on driving behaviour: A driving simulator study. Journal of Environmental
Psychology, 29(4), 493-502. Bella, F. (2005). Validation of a driving simulator for work zone design. Transportation Research Record: Journal of the Transportation Research Board, 1937(1), 136-144. Clarke, D. D., Ward, P., Bartle, C., & Truman, W. (2006). Young driver accidents in the UK: The influence of age, experience, and time of day. Accident Analysis & Prevention,
38(5), 871-878. Hagenzieker, M. P., Commandeur, J. J., & Bijleveld, F. D. (2014). The history of road safety research: A quantitative approach. Transportation research part F: traffic
psychology and behaviour, 25, 150-162. Jiménez-Mejías, E., Prieto, C. A., Martínez-Ruiz, V., del Castillo, J. D. D. L., Lardelli-Claret, P., & Jimenez-Moleon, J. J. (2014). Gender-related differences in distances travelled,
driving behaviour and traffic accidents among university students. Transportation research part F: traffic psychology and behaviour, 27, 81-89. Moridpour, S., Rose, G., & Sarvi, M. (2010). Effect of surrounding traffic characteristics on lane changing behavior. Journal of Transportation Engineering, 136(11), 973-985. Olstam, J. (2009). Simulation of surrounding vehicles in driving simulators. Pampel, S. M., Jamson, S. L., Hibberd, D. L., & Barnard, Y. (2015). How I reduce fuel consumption: An experimental study on mental models of eco-driving. Transportation
Research Part C: Emerging Technologies.
IS VEHICLE CONTROL AFFECTED BY SURROUNDING VEHICLES? (A DRIVER SAFETY PERSPECTIVE)Name: Adesina AdelusiName: Adesina AdelusiMSc (Eng) Transport Planning & EngineeringEmail: ts14aoa@leeds. ac.ukSupervisor: Dr Daryl Hibberd
Road type Lead vehicle type Following vehicle driver
Rural road Car Male
Heavy vehicle Female
2. Experiment Design• The desktop driving simulator experiment design as described in Table 2 includes a
road type, traffic composition and a series of traffic events being presented to theparticipants.
• There are two main scenario where the traffic events will be presented to theparticipants . Each scenario should last about 20 minutes including a 5-10 minutesfamiliarization time.
• A distraction event is also being considered.
Simulator drive Scenario car vs car Scenario car vs HGV Scenario Events
Participants will drive
on a Rural roadBase line (normal
drive) and
treatment drive
(events drive)
Base line (normal
drive) and treatment
drive (events drive)
Aggressive driving
behaviour and violation
including:
• Speeding & overtaking,
• Weaving (drink & drive)
• Running the stop sign.
*Distraction sub task?
5. Conclusion• The outcome of this study is expected to follow similar trends as in previous studies
on the effects of driving behaviour on other road users.• It will be interesting to observe the pattern of the data collected.• Male drivers are expected to react differently to female drivers while heavy vehicles
are expected to have more effect on participants driving performance.
• Aggressivebehaviour and
• violation
• Rural roads“accounts for 2/3of road deaths inthe UK” (RRCGB,2013)
• Cars
• Heavy Vehicles
• Longitudinalcontrol(Headway)
• Lateral Control(Lane change/positioning)
Vehicle
Control
Surrounding
Vehicles
Driverbehaviour
Road type
Figure 5, Factors contributing to young drivers accident (RRCGB, 2011). Figure 6, Accident involving young car drivers aged 17-24 in 2012 per millionpopulation (RRCGB, 2012)
Figure 3, Interaction contributing to accident cause (Lai, 2014). Figure 4, Comparison of available experiment methods (Lai, 2014).
Figure 2, Desktop driving simulator and its capabilities
Figure 1, Typical driving situation on a rural road in the UK (Riley, 2014).
Table 1: The fundamental basis for this research
Table 2: Experiment design to be implemented in the driving simulator
Experiment
Design
Participant
Recruitment
SimulatorData
Collection
DataAnalysis
Understanding Choice of Departure Airport and its Relation to Surface Access
A Case Study of London Gatwick and London Stansted Airports
Problem:Currently, airport surface access in the UK is heavily reliant on trips by private car, which has resulted in congestion on local road networks and raised levels of pollution from vehicle emissions.
57.2%42.6%
Mode Share to London Gatwick Airport
Private Transport Public Transport
48.3%51.5%
Mode Share to London Stansted Airport
Private Transport Public Transport
Both airports are the artery for short haul and
point to point flights across Europe which may
have similar travel pattern.
Majority of the catchment area of both airports
are from South East of England.
Both airports have a good score in public
transport mode share!
To understand what is most important to air
passengers when making their travel decisions.
To understand how the current surface access to
London Gatwick and London Stansted airports
influence passengers on selecting departure airport.
To understand the relationship between
demographics of airport passengers and their choice
of departure airport with their preferred mode of
transportation.
To model the current car parking charges and public
transport fares at both airports and evaluate the
effects on mode shares.
Research Objectives
Methodology
Structured interviews to be performed on individuals
particularly flown from either two of the survey airports to
collect demographic information such as age, car ownership etc
with their respective transportation mode to airport. Besides
that, comments from respondents to gain insight into the current
issues related to surface access to airport that are not known to
the researchers.
Data can be collected either in the departure lounge of airport
or in the train (provided with access permission), or from streets
of both airports catchment area if access to the restricted area
is denied. Sampling methods are carefully evaluated to avoid
sampling bias.
Passengers Survey and Catchment Analysis data from UK Civil
Aviation Authority (CAA) could be used as Revealed Preference
(RP) data to provide deeper understanding regarding the
preference of departure airports.
Fares information such as airport parking charges and public
transportation fares can also be collected through related
authority and online. London Gatwick and London Stansted Airports?
Supervisor: Bryan MatthewsVincent Chan
Best P.T. Mode Share to Airport
in the UK!
What makes you buy a particular air ticket?
Airports locations?
Cheapest Ticket from A to B?
Quickest way? Most convenient?Airlines?
Choice of destination and airfare are the most important
drivers of airport choice.
Access costs and time are the least important.
Key findings from previous research:
References Budd, T. et al. 2011. Airport surface access in the UK: A management
perspective. Research in Transportation Business & Management. 1(1), pp.109-117.
Johnson, D. et al. 2014. Understanding air travellers' trade-offs between connecting flights and surface access characteristics. Journal of Air Transport Management. 34, pp.70-77.
The Impact of High Speed Rail on Tourism− A Case Study of Shanghai
Figure 1: Long-term trend line of Shanghai domestic tourist volume in the past 14 years
0
5000
10000
15000
20000
25000
30000
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Poster: YIFAN WANG ([email protected])Programme of Study: Msc Transport PlanningSupervisor: BRYAN MATTHEWS
Figure 2: High Speed Rail in Shanghai
BackgroundMost researches about the impact of High Speed Rail (HSR) ontourism have focused on Europe (e.g. France and Spain), and themajor direction of these studies explores whether the HSR servicecan be a key factor to influence the choice of the destination fortourism (Francesca et al., 2015; Marie et al., 2014). However, thestudy area of the impact of HSR on actual tourist volumes and somespecific tourist travel behaviour is rarely discussed.
HSR is developing rapidly in China, especially in several mega cities,such as Shanghai, Beijing, etc., however, there are only a fewstudies that refer to this topic, and most of them are just based ontheoretical descriptions. Therefore, my research will mainlyconcentrate on whether HSR can affect the tourist travel behaviourand actual tourist volume in the Chinese tourism market, and howto make the service better to improve the tourist industry with acase study of Shanghai.
Objectives1) Discuss the relationship between HSR and tourism based on a
review of literature.
2) Two sub objectives based on the case of Shanghai:
Examine the travel behaviour of domestic tourists influenced
by HSR through an online survey.
Examine the impact of HSR on domestic tourist volume in
Shanghai through the Tourism Background Trend Line (TBTL)
model.
3) Put forward some recommendations to make HSR serve the
tourism market better in China.
MethodologyProposed scope: The data being used in this case will bedomestic data. According to Francesca et al. (2015) and Marieet al. (2014), the impact of HSR is mainly to influence domestictourists, and this effect will be more significant in Chinabecause there is almost no international HSR lines so far. Inaddition, the TBTL model is mostly widely used on domestictourism (Li, 2009; Liu et al., 2012; Zhang et al., 2013).
1) Online survey Targeted group: people who don't live in but have travelled
to Shanghai at least once in the previous 2 years; Proposed key data to be collected (relate to questions):
travel purpose, origin, route choice, transport mode choice,personal information (e.g. age, income, education, etc.),travel frequency, travel scope and duration time.
2) TBTL ModelThis model is most widely used in domestic tourist marketresearch in China, which was put forward by Gennian Sun in1998. The key data we need in this case is the number ofdomestic tourist travel to Shanghai every year, which can beaccessed from Shanghai Statistical Yearbook (2000-2014).
The Anticipated ResultAccording to the references, in most cases, HSR does influence thedestination choice of tourism, therefore, the result of this study isexpected that HSR will have an impact on both tourist travelbehaviour and domestic tourist volume in Chinese tourism market tosome extent.
Main References:Francesca, P. et al. (2015). High Speed Rail and the Tourism Market: Evidence from the Madrid Case
Study. Transport Policy. 37, pp.187-194.
Marie, D. et al. (2014). Can High Speed Rail Foster the Choice of Destination for Tourism Purpose?
Procedia – Social and Behavioral Science. 111. pp. 166-175.
Liu, C., Wang, L. and Yang, A. (2012). Research on Inbound Tourist Market of Liaoning Province Based
on Tourism Background Trend Line. ICICA 2012, Part 1, CCIS 307, pp. 783-788.
Zhang, W. et al. (2013). Study on the Impact of High Speed Railway on Urban Tourism – Taking Nanjing
as an Example. Economic Geography. 33(7), pp.163-168.
Li, Z. (2009). A Research on the Foundation and Application of the Background Trend Line of Domestic
Tourism in China. Statistics and Information Forum. 24(1), pp.62-65.
Research on Capacity Reduced by Taxi Picking Up on Curb Parking Facilities
Presenter: Yihang Liu Email: [email protected] Msc (Eng) Transport Planning and Engineering Supervisor: Dr. Haibo Chen
Background
According to DFT (2013), there were an estimated 78 thousand taxis in England and Wales at end March 2013 and the grow ing rapidly from 1985 (see figure right).
In most major cities, the taxi is a more convenient mode due to its speediness, door‐to‐door attribute, privacy, comfort, long‐time operation and lack of parking fees.
The layout of harbor‐shaped taxi stop has negative impact on the road capacity, as the limited number of parking space leading the other taxis should occurs queuing frequently and block one lanes of the urban road (see figure), which causes extra delay and the congestion on the links. So that, this work is going to model the probability of the queue happened and the road capacity reduced. Furthermore, calibration of the formula is obtained with the survey data, and validation is comparison between the micro‐simulation software results and the calculated results.
Objective
This work aims to evaluate the harbor‐shaped taxi stop impact on the capacity reduction in urban area and obtain a formula to express the rule of actual flow.
Data collection
Time: afternoon peak periodFacility: video cameraData category：Spot speed, Arrival flow, Arrival taxi flow,
Taxi stop time, Taxi stop layout
Methodology
Data Analysis &Expected Results
The Gamma function should suit for the arrival taxi rate and service rate to obtain the variable for the next queuing theory.The probability of with and without queuing should be stable, acting as the weight for capacity derivation.After derivation process, the results calculated by capacity formula should be close to the micro‐simulation results.
A comparative study of Transport Investment Appraisal Tools and
their implications on project selection
Yvonne M Keinembabazi (MA Transport Economics) | Dr James Laird (Supervisor) | Dr Astrid Gühnemann (2nd Reader)
4. DATA
5. METHODOLOGY
7. Key Reference
0
5
10
15
20
25
30
35
40
45
50
Engineering
Scores
Local
Consult
Scores
Economic
Scores
Composite
Scores
Qu
an
tity
Ranking System
Top Ranked Projects Selected with a $5 Billion Funding Pool
No. of Projects Selected
Aggregate Jobs Added(000)
Aggregate GDP Added(Billion Dollars)
Total Wider Benefit(Billion Dollars)
r = 1 −6∗ 𝑑2
𝑛 𝑛2−1
To compare the rankings, the sign of the Spearman correlation will determine the direction of association between the CBA rankings and GRP+B rankings.(determining whether they are in agreement or not)
Spearman’s rank correlation coefficient
WEISBROD, G. Incorporating economic impact metrics in transportation project ranking and selection processes. Annual Conference of the Transportation Research Board, 2011.
To investigate whether there is a significant difference between
project rankings recommended by BCA and GRP/$
Are projects with a more inclusive and environmental focus likely to
be neglected when GRP/$ prioritization method is the basis of
investment decisions?
Does GRP/$ prioritization overlook a substantial proportion of
benefits provided by projects?
Is GRP/$ prioritization equivalent to Benefit-Cost Analysis?
There is a range of techniques to prioritize transport projects.. Cost- Benefit Analysis (CBA) has been the most commonly used
appraisal tool in Europe, Australia and some states in USA (Benefit-Cost Analysis). Frameworks differ by country.
CBA challenges; Rule of a half does not measure all economy impacts from projects
Alternative appraisal techniques Multi-Criteria Analysis Composite rating schemes e.g. Kansas (Engineering, Local consult, Economic) Cost effectiveness e.g. ranking based on GVA/£ e.g. England City Deals (Fully
devolved local transport funds);Urban Dynamic Model in West Yorkshire Each Appraisal tool has different factor weights which may affect project
selection (Weisbrod, 2011)
Overall Economic Impact
Change in Transport
user benefits
(CS)
Change in systems
operating costs (PS)
Change in costs of
externalities
Investment costs
(Including mitigation measures)
3. CASE STUDY: KANSAS, USA
6. COMPARING CBA AND GRP+B RANKNGS
Data from Kansas Department of Transportation
Systems operating cost
Investment Costs
Estimation of externality costs
Estimation of user benefits
California Life-Cycle Benefit-Cost Analysis Model
Estimation of costs and benefits over
the appraisal period (20 years)
Apply Discount
rate (CalTrans=4.0) Calculation of NPV, BCR and IRR
Presentation of CBA rankings
Presentation of rankings based on economic impact score (Kansas DOT)
Compare CBA rankings and GRP+B rankings
• Data on 121
highway
expansion
projects provided
by Kansas DOT
Data Set includes; Traffic data Highway design
(Speed, length, lanes) Highway accident
data Project costs
1.MOTIVATION
Kansas Composite Rating Scheme
Local Consult Score
Economic Score Engineering Score
Based on project impact on traffic flow
Based on feedback heard at local
consultation meetings
Impact on state-wide Gross Regional Product
(GRP) plus value of personal time and safety
benefits
2. OBJECTIVE AND RESEARCH QUESTIONS
VEHICLE HANDLING WITH SHARED HAPTIC CONTROL
Xianshuchang Wu
Supervisor: Hamish Jamson; Andrew TomlinsonInstitute for Transport Studies, University of Leeds, Leeds, U.K.
E-mail: [email protected]
WHAT IS SHARED HAPTIC CONTROL? WHY SHARED HAPTIC CONTROL? Task Automation
Response Automation
Haptic Interface
How does it work?
Hpi
From Pedal Feedback to Steering FeedbackFigure 1. A schematic, symmetric representation of SHC (adapted from Mulder et al., 2012)
Progress towards Haptic Shared Control
MAIN FOCUS OF THIS WORK
Limitation of Previous Work
METHOD / PATHWAY
Hypothesis
Figure 3. Brief illustration for the main experimental process
Mainly Estimated Dependent Measures
Figure 2. University of Leeds Driving Simulator
Incorporating Transport Network Resilience with Building Information Modelling
Background
What is BIM?
Building Information Modeling (BIM) is a digital representation of physical and functional characteristics of a facility. A BIM is a shared knowledge resource for information about a facility forming a reliable basis for decisions during its life-cycle; defined as existing from earliest conception to demolition. In general, it is a graphic tool to make projects virtualized though the whole life-cycle. (e.g. Autodesk Civil 3D and Bently)
What is traffic resilience
1. Resilience of system is a measure of the speed of its return to equilibrium.
2. The perturbation can be absorbed before the system converges on another equilibrium state
Select an appropriate transport project which is disrupt by nature– for example dual carriageway destroyed by flood.
Using the BIM software to simulate the loss on a infrastructure caused by a perturbation.
To Analyze not only the cost on the infrastructure itself but also the direct and indirect economic cost for road users in the whole traffic network cased by perturbation.
Mainly focus on the transport infrastructure damage caused by nature perturbation without casualties. And it can be restructured in short term.
Existing infrastructure built with BIM software participated in before.
Proposed Scope
Methodology
Aims and objectives
BIM software
Cost of rebuild and
the materials
Xian Wu Msc Transport Planning & Engineering Supervisor: Haibo Chen Second Reader: Daryl Hibberd
Transport Software
Road users delay and the
detour distance
Total Impact
BIM software can provide the materials needed during the restructured then calculate the cost for this restructured
After perturbation the traffic network will be influence continuously influenced until it is totally repaired. All of the impact by perturbation can be simulated by transport software. Such as the cost of extra time and longer distane on passengers and some kind of environmental emissions caused by detour
Institute of Transport Study
What can we know about changing multi-modal travel behaviour?
—Xiaojun Shao, MSc(Eng) Transport Planning and Engineering Supervisors: Caroline Mullen, Giulio Mattioli
Background
In National Travel Survey (NTS) 2012, an index chart shows that between 1995/97 and 2012 the average distance of car/van driver trips and passenger trips has fallen by 7% and 12% respectively. This decline in per capita car travel has attracted people’s attentions. For instance, a roundtable meeting organised by the New Zealand Ministry of Transport on this topic was convened in London on 20 May 2014. They believe that the demand for car travel is reaching its saturation level, any further growth will give little benefits for travellers (Lyons and Goodwin, 2014). Therefore, a development of other modes of transport is necessary in supporting the benefits of travellers.
Meanwhile, although there is a saturation in car use, the traffic congestion problems still exist. One of the solutions transport policy is seeking for is to encourage the use of alternative modes of transport, such as cycling and walking (Ogilvie et al, 2004). For example, some policies such as car sharing and bike sharing are now influencing people’s travel behaviour by encouraging people to travel on multi-modes.
For its definition, there are different understandings. Nobis (2006) describes that all persons who within 1 week use at least two different transport modes are defined to be multimodal; Kuhnimhof (2006) believes that it is a property of travel demand. No matter how many definitions exist, the importance of multimodal travel is to make people rely less on private cars. Therefore, it can be explained as a characteristic that people use modes other than just the car across their travel patterns.
But what exactly is multimodal travel?
To help governments and local authorities shed lights on multimodal travel, an understanding of how people are travelling these days and whether they are using only one mode are necessary.
Furthermore, two key questions need to be answered:• Does the NTS provide this understanding?• How can the NTS or other surveys be improved to give a better
understanding?
Objectives
In realistic, multimodal travel may include every available transport mode, but in this dissertation, only the choices between three groups will be used, they are driving a car, using public transport (excluding airlines and ferries), walking and cycling. Because these are the most common modes people use to travel inside a city.
Scope
Methodology
The primary methods used to investigate the trend of multimodal travel are literature survey and questionnaire. The scope of literature survey includes papers that link multimodal travel to congestion management. For questionnaire method, there are three steps could be taken in order to fulfil the investigation:• Identify the gap and limitation of multi-modal travel in the questionnaire
used in National Travel Survey;• Determine what questions should be included and provide options for
participants to choose;• Decide the sample size of the survey and provide the questionnaires online
for students and staff in ITS and other departments.
For the sample size, Peter et al. (2011) had a study on European multimodal journey, they designed a questionnaire contains 18 questions and put it online for people to participate. In the end, they have 200 responses in total which provides an effective result. Therefore, a roughly 200 participants are expected when doing the dissertation.
The analysis will be done with data mainly from National Travel Survey.
Data
Expected Findings
UNIVERSITY OF LEEDSInstitute for Transport Studies
• The NTS is an established series of household surveys of personal travel and it has been running continuously since 1988. This study will mainly use the data between 2002 and 2012 to analyse the trends.
• NTS data is collected via two main sources - interviews with people in their homes, and a diary that they keep for a week to record their travel. It covers travel by all age groups, including children.
An example of how British people travelled in 2012
From literature and data analysis, these are the results I expect to see:• Develop a method to determine whether people are becoming more
multimodal.• Multimodal travel can relief traffic congestion to some extent.• The newly designed questionnaire can more capture people’s mode
choice of travel than the travel diary used in NTS.
Night-time Driving and Distraction Xue Ding. MSC Transport Planning. Supervisor: Georgios Kountouriotis
E-mail Address: [email protected].
Night – time driving expose to higher risk to
accident than day time. Number of miles driven
decreases substantially at night compared with
daytime, yet more than half of all traffic deaths
occur after dark.
Is driving distraction contribute to this increase
in accident?
This research uses driving simulator to collect the
driving performance data and then compare the
influence of different factors to driving
performance.
Prediction
Comparing with day-time driving, eye-
movements (PRC) of night-time might rise due to
the dark view.
Steering wheel reversal rate in bend road is easily
affected by distraction than straight road
Visual distraction produced by in-vehicle
information system has more significant
influence on SDLP than visual distractionn on
road centre.
References Plainis, S., Murray, I. J., & Pallikaris, I. G. (2006).
Road traffic casualties: understanding the night-
time death toll. Injury Prevention, 12(2), 125-138.
Pettitt, M., Burnett, G. E., & Stevens, A. (2005).
Defining driver distraction. In12th World
Congress on Intelligent Transport Systems.
Stutts, J., Feaganes, J., Reinfurt, D., Rodgman, E.,
Hamlett, C., Gish, K., & Staplin, L. (2005).
Driver's exposure to distractions in their natural
driving environment. Accident Analysis &
Prevention, 37(6), 1093-1101.
Merat, N., & Jamson, A. H. (2008). The effect of
stimulus modality on signal detection:
Implications for assessing the safety of in-vehicle
technology.Human Factors: The Journal of the
Human Factors and Ergonomics Society,50(1),
145-158.
Time
Road
Task
Day-time
Night-time
Straight road
Bend road
Visual (Center)
Visual (IVIS)
Count back
Baseline (No Task)
Distraction source % of drivers Outside person, object. events 29.4
Adjusting radio, cassette, CD 11.4
Other occupant in vehicle 10.9
Moving object ahead 4.3
Other device/object brought into vehicle 2.9 Adjusting vehicle/climate control 2.8 Eating or drinking 1.7
Using/dialing mobile phone 1.5
Smoking related 0.9
Other distraction 25.6
Unknown distraction 8.6
Percentage of driver who cited each distraction
source as contributing to crashed
Total number of
participant 20
Age 20-30
Gender 10 male & 10 female
Driving experience Over 2 years
Preparation before
experiment
Provided with written
instructions about the
experiment
Driving time in
experiment 30 minutes
Methods
University of Leeds driving simulator will be employed to mimic driving with different factors
Fig. 1. The University of Leeds Driving Simulator
Fig.2. night-time view in driving simulator (urban & rural)
• Steering wheel reversal rate
• Standard deviation lateral position (SDLP)
• Percentage of road centre (PRC)
• Data analysis tool: SPSS
• Data analysis method: Repeated Measures
ANOVA
Introduction
Distraction is “attention given to a non-driving-
related activity. Typically to the detriment of
driving performance”
Driver distraction plays an important role in
crash
Simulate
SATURN
Scenario 3
Adjusted Capacity Network
2009 Existing Leeds OD Matrix
Optimal Signal Plan from LINSIG
Scenario 1 (Base Scenario)
2009 Existing Leeds Network
2009 Existing Leeds OD Matrix
2009 Existing Leeds Signal Plan
Scenario 2
Adjusted Capacity Network
2009 Existing Leeds OD Matrix
2009 Existing Leeds Signal Plan
Find Optimal Signal Plan
using LINSIG
Simulate
DRACULA
SATPIG SPATULA
Detailed Public Transport Modelling of Bus Frequencies, Bus
Stop Locations etc.
Adjust the Road Supply
Condition/Capacitydue to Road Work
in Network.dat
Comparative analysis
of outputs from Scenario Runs
SATURN LINSIGDRACULA
2. Data
University of Leeds and Leeds City Council provided:
The SATURN model and data files have been constructed according toWebTAG recommendations and validated against DMRB guidelines).
6. Scope and Data Analysis
Win Thi Ha , MSc (Eng) Transport Planning & Engineering Supervisor : Dr Chandra Balijepalli
1. Background and Motivations
• Private and Public Transport Road Users suffer from delays, congestionand unreliable journey times due to regular road closure to maintain andimprove old infrastructures and road system in the UK to meet theincreasing travel demand.
• More frequently digging up the roads by utility companies (Gas, Water)• Government recently announced 55 major road schemes and local
transport projects with a further 15 billions spending between 2015-16and 2020-21.
• Part of proposed 14.8km NGT(Trolley Bus) route - Otley Road(A660) section from the RingRoad (A6120) Roundabout to thejunction of North Lane/WoodLane in Leeds, West Yorkshire.
A “quasi” dynamic element will be introduced into runs of SATURN bymodelling three successive AM time periods to include the effect of thedeparture time choice.
Literature Review
• Evaluation of Traffic diversion plans
• Traffic modelling softwares
• Monetary cost of congestion and delay due to road works
Implement different scenarios
• Link and Convert output route flows to facilitate interface with DRACULA fromSATURN Assignment O-D route flows using SATPIG and SPATULA programs.
• Adjust Road Capacity on planned road work routes according to diversion plan
• Develop LINSIG model to optimise and coordinate signals within study cordonarea.
Simulation results and
data analysis
• Comparative analysis of Modelling Scenarios Results on the effects of the roadwork on private vehicles and public transport buses primarily at Micro level.
• Analysis of Measure of Effectiveness on worst congested junctions/ links/ nodesat Macro level across Leeds Network in general.
Evaluating traffic diversion plan due to road works and assessing the impact on private vehicles and public transport buses
Institute for Transport Studies
Image © Copyright Descry and licensed for reuse under a Creative Commons Attribution-ShareAlike 2.0 Generic (CC BY-SA 2.0)
In Leeds Area alone during 2012-2013:• 6,279 road works with average of
4.98 days• 31,269 days of disruption
Source: Mitchell, 2014 (Leeds City Council Report)
• 830 Zones, 3034 Nodes.2009 Leeds Network
• 467,630 Total Flow, Three AM time periods (7-8 , 8-9 and 9-10 AM).
2009 Leeds Trip Matrix
• Route , Traffic volume count, Speed, Distance. 2009 Validation Count
References:Goodwin, P. 2005. Utilities’ street works and the cost of traffic congestion. Research Report February,p.37. Centre for Transport &Society, University of the West of England, Bristol.Mitchell, P. 2014. Leeds Permit Scheme for Road Works and Street Works. Annual Report 2012-13.Zhou, H. 2008. Evaluation of Route Diversion Strategies Using Computer Simulation. Journal of Transportation Systems Engineeringand Information Technology. 8(1),pp.61–67.
Cordon Network
Number of Zones 34
Number of Nodes 88
Simulation Links 192
Number of Signal Stages 30
Number of Roundabouts 3
Priority Junctions 52
Traffic Signals 9
Total Traffic Flow (Actual) 3357
4. Objectives
• To Minimise the impact and effect on private vehicles and publictransport buses due to road work.
• To Optimise signals of roundabouts and junctions within studycordon area.
• To Understand positive/negative impacts of optimised signals byanalysing computer traffic simulation softwares outputs
• To Evaluate the traffic diversion plan and the effect on private andpublic transport buses at Micro, Meso/Macro Levels.
5. Methodology
• Methodology itself is generic and widely used in local, regional &national Traffic Management Centers.
• Implementing 3 different scenarios based on 2009 Leeds Network,Signal Plan and Trip Matrix data.
3. Study Cordon Area.
Figure 1: Cordoned off Leeds Network (Maps created using ArcGIS® software by Esri)
Email: [email protected]
In the UK:• 7 millions days of disruption• Valued at £1bn – £4.3bn
(Reports & Studies widely quoted)
• 5-10% of total congestionSource: Goodwin, 2005
Special events /other
5%
Bottlenecks
40%
Road works
10%
Traffic Incidents
25% Poor traffic signal timing
5%
Bad weather
15%
Source: www.ops.fhwa.dot.gov
What Safety Policies Should Accompany the Goal of Achieving MoreSustainable Urban Mobility: An Examination of Problems and
Policies in EuropeTaner Ulug, (MSc) Transport Planning and Engineering
Supervisor: Prof Oliver Carsten
UNIVERSITY OF LEEDS
Background•European Union plans to achieve an overallsustainable transport system in order to decreasepollution and congestion.
•Sustainable urban mobility is a vital part of thisplan.
•About 40% of all road accident fatalities in the EUoccur in urban roads.
•11,000 deaths in 2012 on EU urban roads.
•65% of all urban road fatalities in the EU areVulnerable Road User (VRU) fatalities.
•A large proportion of serious road injuries occurin urban areas and and involve VRUs.
•VRUs: Pedestrians + Pedal Cyclists +Motorcyclists&Moped Users
•VRU safety needs to be improved in order toachieve sustainable urban mobility.
United Kingdom‐Urban Source: CARE Database
Objectives•To determine best performing three EU membercountries in terms of VRU safety on urban roads sinceyear 2000.
•To determine for which three main VRU modes thesecountries have performed beter.
•To discuss the VRU safety policies which have possiblycontributed to the good performance of thesecountries.
Data Collection•Secondary data will be acquired for years since 2000.
•Community Road Accident Database(CARE) will be utilized for this purpose.
Methodology1. Analysis of annual changes in fatalities as reportedby transport mode in EU countries on urban roads,rural roads, and motorways.Analysis of annual changes in VRU fatalities by agegroups and gender.
2. Determination of best performing three membercountries in terms of VRU safety with a focus onurban roads.
3. Determination of how these countries hasperformed when other parametres such as agegroups, gender and VRU transport modes areconsidered in order to understand the exact issuesthese countries have tackled well.
4. Investigation of VRU safety policies implementedby these countries particularly before the yearswhen there have been significant achievementsregarding the issues mentioned above.
Expected OutcomeThe best performing three EU countries are expectedto be the SUN(Sweden‐United Kingdom‐Netherlands) countries, but Denmark may replace the Netherlands.
Successful policies are possibly developed under thefollowing VRU safety issues;•Investing in safer urban infrastructure•Use of modern technology for enhanced urban roadsafety•Traffic rule enforcement and road safety education
Photograph Sources: Road Safety in the European Union, Vademecum_2015
As a consequence of the arid conditions, PM dispersion from the region is hindered and secondary process such as wind driven resuspension dominate. This means that while gas-phase species associate with their primary sources (e,g. traffic levels), PM does not.
In 2010 air pollution was estimated to have caused over 400,000 premature deaths in Europe. Ambient air pollution was estimated to cause 3.7 million premature deaths worldwide in 2012.
2. MECCAMecca is a major centre for tourist and religious pilgrimage in Saudi Arabia.As in many cities, local air pollution is affected by multiple inputs, including emissions from traffic, construction work, industrial practices, etc.However, arid conditions make it especially sensitive to particulate matter (PM) pollution.
3. PROJECT DATAIn this project Air Quality data (including CO, NO/NO2, and PM10) and PM compositional data (anions, cations, and metals) collected by Professor Turki Habeebullah and colleagues at Umm Al-Qura University, Makkah, will be analysed with the intention of extending understanding of local air quality in the region.
4. OBJECTIVES/METHODSThe study will proceed as follows:i) Use R and R package openair to characterise local air
quality data, andii) Use specialist software, including US EPA UNIMIX , to conduct the first source apportionment of the dataset.
Trophius Kufanga. Msc Transport Planning & the Environment. [email protected]
References:
5. RESULTS
6. NEXT STEP: SOURCE APPORTIONMENT
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Improved Air Quality Management for Makkah Al-Mukarramah (Mecca), Source Apportionment of Air Quality and Particulate Composition Data
Supervisor: Dr. Karl Ropkins 2nd reader: Dr. Haibo Chen
Some Preliminary Findings:
The Saudi Arabian PM10 standard 340 ug.m-3 daily average,not to be exceeded more than 24 times a year. In 2012, this was exceeded 32 times.
However, unlike in UK, where PM10 standards are also regularly exceeded, this was not associated with NO2 exceedances, highlighting the different nature of the air quality problems in Makkah.
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09/15 /201209/27 /201210/09 /201210/27 /201211/09 /201212/03 /201212/22 /201201/26 /201302/07 /201302/19 /201303/09 /201305/20 /201308/06 /2013
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UNMIX source apportionment of PM composition trends, which are not affected by resuspension will help us to identify PM sources.
By contrast, PM10 associates with higher wind speeds, in particular from the South East
Many gas phase species, like NO2,associates with low wind speeds, an indication of local stagnant air related sources
Hitchcock, G., et al. (2014) Air Quality and Road Transport. Impacts and solutions. RAC Foundation. London, United Kingdom.WHO (2014) Ambient (outdoor) air quality & health
High Volume Systems (HVS PM Samplers)
Ion ChromatographyAnions and Cations
1. GENERAL BACKGROUND
・Categorize questioners →social economics (gender, age and employment state) →general impression of PTP (how does PTP make you feel) →interest for PTP/level of satisfaction of PTP (how are people satisfied with PTP) →modal changes (how do people change into use of public transport) →interest for sustainability (continuous of new travel behavior) ・Using regression analysis →how is effectiveness of PTP related with questioners? →For example, how much effectiveness of PTP is linked with age or gender? Is there any difference in the effectiveness between women and men?
・To know who changes travel behavior ・To know how they change travel behavior ・To know why they change travel behavior ・To know how the impact of PTP can be measured
・ Follow up survey to determine the influence of PTP on travel behavior ・10 different cities in the UK from 2009 to 2014 ・4786 data of PTP in those areas
・7-15% decrease in car trips can be expected ・12% reduction in the mean distance travelled by car ・increases in walking, cycling and public transport trips of between 14% and 33% ・effectiveness of PTP would last about 3 years
Because of increase in cars… →environmental problems (increase in CO2) →health problems (effect on respiratory) →traffic problems (congestion) Introduction of PTP What is PTP ? ・PTP is one of the methods of soft measures ・Through one to one conversation with trained field officers ・Officers encourage and motivate people to change their travel behavior by giving provision of information on how to travel sustainably ・Useful information and good are given such as time table for each person or free trial public bus tickets
Who changes travel behavior and why ? Tomoko Amahori : MSc Transport Planning and the Environment Supervisor: Jeremy Shires
Backgrounds
Effectiveness of PTP
Data of PTP
Objectives
Methodology
Can Development on the Green Belt be Sustainable?
BACKGROUNDGreen belt is open space used for forestry and agriculture.In spite, its importance for environment, some localauthorities change the land use for construction ofresidential, industrial and other projects. One of the mostcommon reason for changing land use is to facilitate theeconomic growth of the region and meet increasingdemand for affordable houses among people at theexpense of the Green belt. This study will attempt tomeasure Sustainability of the Development on the Greenbelt and assess Transport impact. The housing developmentof 4020 dwellings on the North of Clifton Moor and A1237will be considered for assessment. It will be located on 330of acres of Greenbelt land.
AIMTo investigate whether development on the Green belt can beSustainable.
OBJECTIVES• To assess Sustainability of the Development on the Greenbelt
• To assess the Transport Impact Assessment on NewHousing proposal on the North of York on the Green belt.
METHODOLOGY• Review of the polices, guidelines and planning documents related to Transport Assessment and Sustainability Assessment.
• Define criteria and alternatives in MCA .• Define appropriate technique of MCA • Multi criteria analysis of Sustainability.• Analysis of findings from MCA.• Analysis of existing SATURN road network of York City.
• Estimation of new trip projected values for trip rates with the use of TRICS, TRIPS and TEMPRo software.
• Updating SATURN OD matrix and network files.• Assessment of public transport accessibility.• Traffic Impact Assessment of the Proposed Development with SATURN software.
• Development of recommendations for mitigation from impacts.
EXPECTED RESULTS• Identification of impact from Transport.• Sustainability appraisal of the development on the Greenbelt.
Supervisor: Dr. Chandra Balijepali Student: Talgat Abdrakhmanov Email: [email protected]
Preparation of Transport Assessment
Final Transport Assessment
Reducing the need to travel
Maximizing Sustainable accessibility
Dealing with Residual trips
Mitigation measures
References: 1. Multi‐criteria analysis: a manual. DCLG, 2009. 2. Guidance on Transport Assessment. TfL, 2007.
Policy contextExisting Site function
Proposed Development definition
Identification of Impacts and mitigation measuresNATA AssessmentCapacity AssessmentIdentify problems
Preliminary design of mitigation measures
Scoping studyInitial appraisal consultation form
Scoping studyAgreement of methodology
Background data
Existing travel patterns by modeAccident history
Environmental base casePassenger transport servicesCommitted developmentCommitted transport network
chargesParking availability
Refinement step 2Where appropriateAdditional support
Alterations to ITB measures
Refinement 1(where appropriate)Seek to reduce residual trips
Review:Development mixScale of development phasing
Measures to influence Travel behaviorParking availability and ManagementImprovements to non‐car modelTravel plan initiativesCapacity ManagementNetwork alterations
AssessmentTrip generation by modeAccessibility AssessmentAssignment of trips
Source: Transport Assessment Guidance. TfL, 2007.
5. Expected Outcomes 4. Preliminary Results
Global travel demand contributes to the increase of fuel consumption in airlines.
U.S. airlines are the main contributors (18 billion gallons). No alternate energy, so policy-making to manage the fuel
demand is important.
Decomposition Analysis of Aviation Fuel Demand of U.S. Airlines
Shan-Che Wu | Institute for Transport Studies | Transport Planning and Engineering | Supervisor Zia Wadud
1. Background
Year Passenger (million) Freight (million tons)
1991 461.2 9.0
2013 748.5 12.3
Growth 62% 37% (Airlines in the U.S.)
2. Objectives To find some components linking the travel with fuel
consumption To decompose the fuel demand into various components with decomposition model To initiate analyzing the freight-related factors To set a freight forecast demand model
Multiplicative decomposition
-
5
10
15
20
25
1991 1994 1997 2000 2003 2006 2009 2012
Fue
l (b
illio
n g
allo
ns)
Fuel consumption of airlines in the U.S.
Total Passenger in Passenger aircraft
Belly freight Freight in freight aircraft
3. Index Decomposition Analysis
Fuel = Population(POP) × REV.ton.miles per capita ÷ Load factor × Efficiency
𝐹𝑢𝑒𝑙 = 𝑃𝑂𝑃 ×𝑅𝑇𝑀𝑃 𝑃
𝑃𝑂𝑃×
𝐴𝑇𝑀𝑃 𝑃
𝑅𝑇𝑀𝑃 𝑃×
𝐹𝑢𝑒𝑙(𝑃𝑃)
𝐴𝑇𝑀𝑃 𝑃 --- passenger in passenger aircraft
+𝑃𝑂𝑃 ×𝑅𝑇𝑀𝐹 𝑃
𝑃𝑂𝑃×
𝐴𝑇𝑀𝐹 𝑃
𝑅𝑇𝑀𝐹 𝑃×
𝐹𝑢𝑒𝑙 𝐹𝑃
𝐴𝑇𝑀𝐹 𝑃 --- freight in passenger aircraft
+𝑃𝑂𝑃 ×𝑅𝑇𝑀𝐹(𝐹)
𝑃𝑂𝑃×
𝐴𝑇𝑀𝐹(𝐹)
𝑅𝑇𝑀𝐹(𝐹)×
𝐹𝑢𝑒𝑙(𝐹𝐹)
𝐴𝑇𝑀𝐹(𝐹) --- freight in freight aircraft
Logarithmic Mean Divisia Index (LMDI) is of better performance
Additive decomposition
and ∆𝐹𝑢𝑒𝑙 = 𝐹𝑢𝑒𝑙𝑡 − 𝐹𝑢𝑒𝑙0
𝐹𝑢𝑒𝑙𝑡
𝐹𝑢𝑒𝑙0=
𝑃𝑂𝑃𝑡
𝑃𝑂𝑃0×
𝑅𝐸𝑉. 𝑡𝑜𝑛. 𝑚𝑖𝑙𝑒𝑠 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑡
𝑅𝐸𝑉. 𝑡𝑜𝑛. 𝑚𝑖𝑙𝑒𝑠 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎0÷
𝐿𝑜𝑎𝑑 𝑓𝑎𝑐𝑡𝑜𝑟𝑡
𝐿𝑜𝑎𝑑 𝑓𝑎𝑐𝑡𝑜𝑟0×
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝑡
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦0
∆𝐹𝑢𝑒𝑙 = ∆𝐹𝑢𝑒𝑙𝑃𝑂𝑃 + ∆𝐹𝑢𝑒𝑙𝑅𝐸𝑉.𝑡𝑜𝑛.𝑚𝑖𝑙 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 + ∆𝐹𝑢𝑒𝑙1/𝐿𝑜𝑎𝑑 𝑓𝑎𝑐𝑡𝑜𝑟 + ∆𝐹𝑢𝑒𝑙𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦
∆𝐹𝑢𝑒𝑙𝑝𝑜𝑝 =𝐹𝑢𝑒𝑙𝑡 − 𝐹𝑢𝑒𝑙0
𝑙𝑛𝐹𝑢𝑒𝑙𝑡 − 𝑙𝑛𝐹𝑢𝑒𝑙0× (𝑙𝑛𝑃𝑜𝑝𝑡 − 𝑙𝑛𝑃𝑜𝑝0)
Revenue ton miles per capita is the most key factor. Efficiency has been gradually improved to save fuel
because of management and technology Hope to link the aircraft freight demand with
economic factors Fare, journey time, and income might be the most
influential parameters in demand model.
Decomposition analysis summary
1. Revenue ton mile per capita always increasing except 2000-2002 (911 terrorist attack) and 2006-2008 (economic recession).
2. Load factor and fuel efficiency slow the growth rate of fuel use.
3. Most of the changes in fuel consumption due to changes in revenue ton mile per capita.
-6
-3
0
3
6
Ch
ange
in f
ue
l co
nsu
mp
tio
n (
bill
ion
ga
llon
s)
POP RTM/POP 1/Load factor Fuel/ATM
Additive and Multiplicative decomposition in 3-year band: 1991-2011
0.8
1
1.2POP
RTM/POP
1/Load factor
Fuel/ATM
1991-1993 1994-1996 1997-1999 2000-2002
2003-2005 2006-2008 2009-2011
Data sources: Bureau of Transportation Statistics, Department of Transportation in U.S.
Evolution of fuel consumption and its components: 1991-2013; 1991=1.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1991 1994 1997 2000 2003 2006 2009 2012
Ind
ex (
19
91
= 1
.0 b
ase
ye
ar)
POP RTMP(P)/POP RTMF(P)/POP RTMF(F)/POP RTMP(P)/ATMP(P)
RTMF(P)/ATMF(P) RTMF(F)/ATMF(F) Fuel(PP)/ASM(P) Fuel(FP)/ATMF(P) Fuel(FF)/ATMF(F)
Fuel (PP) Fuel (FP) Fuel (FF)
Causative Factors of Accidents on Curve Negotiations: A Case Study of MalaysiaInstitute for Transport Studies
Seri Ashikin Binti Sofian, MSc.Eng Transport Planning & Engineering Supervisor: Dr. Samantha Jamson Co-Supervisor: Dr. Frank Lai
• Traffic accidents rank fifth among the leading causeof deaths in Malaysia.
• It is estimated that, one quarter of all accidentshappen in Malaysia occur while driving aroundcurves and in most cases contribute to fatalaccidents. Therefore, it is vital to understand thefactors lead to an accident that occurs on a curve.
• IRTAD report 2014, based on willingness-to-payestimation, road accident accounted forapproximately 1.6% of Malaysia national GDP.
• The accident rates in road curves are about 1.5 to 4times higher than in straight roads(Zegeer, Stewart, F. M. Council, Reinfurt, &Hamilton, 1992).
• The accident severity of curve related crashes ishigher than those occurring in straight roads(Glennon, Neuman, & Leisch, 1985).
• Accidents are not uniformly distributed on the roadnetwork, high accident locations are a clearindication that, besides human factor, there existother influencing parameters that are characterizedby the road (Lamm et.al, 2007).
• The curve's location chooses for this study areidentified from the 186 blackspot locations treatedunder the ‘Rehabilitation Works Programme’ doneby the Public Works Department of Malaysia(PWDs) from year 2009 to 2014.
• Seven (7) locations of curve are identified from theblackspot locations and data collected from thislocation are gathered through the POL form obtainfrom the Royal Malaysian Police (TrafficDepartment).
Background of Study Determine the factors that contribute to the accident occurrence in a curve
Identify the characteristics to the cause of the accidents occurrence on a curve
Recommendation for road accident on curve treatment
Objectives
Research Questions ?
“What are the factors that have influenced for accidents to happen on a curve”
“Is there a relationship between demographic and road factors which contribute to an accident on curve”
Theoretical Framework
Demographic
• Age
• Gender
Road • Length of
curve• Radius of
curve
Numbers of fatal accident
Methodology
Null Hypothesis• Road factors contribute to an event of an
accident on a curve• Demographic factors influence the driving
behaviour and the occurrence of theaccidents on the curve
• Both, demographic and road is contributoryfactors in an accident on a curve
Alternative Hypothesis• Road factors does not bring impact to
the occurrence of an accident on acurve
• Demographic factors do not influencethe driving behaviour
• Both factors fail to demonstraterelationship their behaviour towards anevent of an accident on the curve
Statistical Analysis
All accidents data obtained from the Public Works Department of Malaysia (PWD Malaysia) and POL Form fromRoyal Malaysian Police will be put through a data cleansing to check its validity and reliability. This is also done inorder to find the demographic information such as age and gender of the driver of the vehicle. This analysis willuse the SPSS package in order to look into the relationship between the variables by using the regressionmodels. The variables of road factors will be studied from seven (7) curve’s location from the blackspot lists,whereas for the demographic factors, 2 locations from this will be analysed.
Data Cleansing
• To check on thereliability andvalidity of thedata
Information Analysis
• Seek demographicinformation fromthe POL form.
• Geometry of thelocation (lengthand radius)
Factor analysis
• Correlation between factors
• Linear Regression (Binary Logistic)
Results
• Significant level of the hypothesis testing
Expected Outcome
• It is expected that the demographic and road factors, will be the factor in an accident oncurve negotiation. Other than that, a significant relationship can be seen from both factor andrelate to the accident occurrence on a curve.
• The findings from this study is yet to be used in the future in order to rectify the accidentproblem that mostly occurs on a curve. On the other hand, this study can suggest for thetreatment and countermeasure to be taken in the road safety enhancement with a focus on acurve negotiation.
Kuala Lumpur – Karak Highway
D e v e l o p i n g A c c e l e r a t i o n M o d e l s C o m b i n i n g M u l t i p l e D a t a
Stavros Papadimitriou (Author); Charisma F. Choudhury (Supervisor); Daryl Hibberd (2nd Reader)
B A C K G R O U N D
I-80 Study Area Schematicand Camera Coverage
Ø Driver behavior data from an artificial scenario in a controlled environment may not resemble driver behavior that is displayed in a comparable real world situation (Carsten et al., 2011)
Ø Calibration and validation in driving simulators generally performed at a macroscopic level (Sakia & Hoogendoorn, 2008) and studies mainly generate macroscopic outputs, (Olstam, 2005) ignoring driver specific information.
1 M E T H O D O L O G Y
d a t a
Driving Simulator Schematic of Road Section
ü X and Y coordinates every 1/10th sec for acceleration decisions of drivers;
ü over a stretch of 1/2 km for an hour (between 16:1517:15);
ü similar traffic density (roughly 1600-2400 vph);
ü 40 subject drivers are recorded;
m o d e l l i n g a p p r o a c h
E X P E C T E D R E S U L T S
c a s e s t u d y
3.2
3
4
NGSIM Driving Simulator
C r o s s – C l a s s i f i c a t i o n A n a l y s i s
S t a t i s t i c a l A n a l y s i s
Maximum Likelihood Method (MLM)
Models format
Responsen (t)= Sensitivityn (t-Tn) x Stimulusn (t- Tn)
Where,- t = time of observation,- Tn = reaction time for driver n,- Responsen (t) = acceleration applied at time t
STATA
Estimation method
Statistical software
Models performance & comparison
Tests of statistical significance (e.g. t-statistics)
3.1
Ø Real-life trajectory data are really important so far for calibration and validation of microscopic models. However, most studies focus on the investigation of lane changing (Thiemann et al., 2008; Ahmed, 1999)
2
Simulation Environment
Physically Driving
Two data sources will be used in this research: (1) The real-life traffic detailed trajectory data collected
from Interstate 80, CA, US (NGSIM 2005); (2) The experimental data collected from the University
of Leeds Driving Simulator (UoLDS).
Microscopic data collected from,(i) Real trajectory data from physically driving;(ii) Driving simulator data from a simulated
environment using a driving simulator.
• Leader speed• Time headway• Type of vehicle• Reaction time etc.
• Leader speed• Gender, Age • Type of vehicle• Reaction time etc.
§ Statistical comparison of the models will indicate significant differences in common model parameters (e.g. leader speed, headway, subject vehicle type);
§ The combined model will better replicate the traffic compared to models developed using single data sources.
The objective of this dissertation is to develop and compare the performance of the acceleration models using two sources microscopic data, as well as testing a combined model using both data sources. Models will take into account network topography and traffic conditions. • Model 1 uses only traffic video data; • Model 2 uses only driving simulator data; • Model 3 uses both.
1000
m10
00 m
2000
m
503
m (1
650
feet
)
Study Area
7 video cameras
O B J E C T I V E S
EMERGENCY TRANSPORT PLANNING FOR MATERNAL HEALTH IN RURAL GHANA
MAHAMA SEINU SEIDU, MSc TRANSPORT PLANNING AND THE ENVIRONMENT SUPERVISOR: JEFFREY TURNER 2ND READER: FRANCES HODGSON
BACKGROUND
REFERRAL SYSTEM
AIM AND OBJECTIVES
METHODOLOGY
EXPECTED OUTCOME
REFERENCES
Thaddeus and Maine,1994
The aim of the study is to assess the impact/effect of Ambulanceservices in maternal health
OBJECTIVES:The study is to focus on understanding and assessing the role ofambulance services in emergency maternal health in Ghana. This isintended to be achieved through : Assessment of the role and impact of Ambulance services in
maternal health delivery in rural areas . Whether or not Ambulance services have any significant
contribution to reduction of maternal mortality. How efficient and effective transport can improve emergency
maternal health intervention in rural Ghana
Millennium Development Goal (MDG 5),maternal mortality isidentifies by the United Nations(UN) as a serious concern for thewelfare of women across the world particularly a pandemic indeveloping countries and specifically an “unfortunate tragedy in subsahara Africa as the region records the highest maternal mortalityratio” (Ganyaglo & Hill, 2012)
About 350,000 women die annually from pregnancy related causesand child birth complications .
Utilization and access to health facilities for maternal services inthese settings is hindered by several factors including lack oftransport and high cost –(4) .Referral intervention aim to addressthese problems and one such intervention is the provision ofemergency ambulance referral transport services.
In most developing countries such National ambulance serviceshave not been sustained effectively, providing very limited, or noservice. As a result, many segments of the population, particularlyin rural or peri‐urban areas are not covered and this poses seriouschallenges to reach the appropriate health facility in case of anemergency.
In Ghana ,the maternal mortality ratio (MMR) is currently 350 inevery 100,000 live births .It is estimated that 75 percent of thewomen who die in the course of childbirth do so as a result ofinadequate emergency transport‐(1).
Transport is critical in the provision of health delivery and access toservices, and in the Overall effectiveness of the referral process.
As have been identified by Thaddeus and Maine(1994), poor accessand lack of reliable transport also explain why families delay inseeking care in an emergency situation or arrive too late at healthfacilities for effective treatment as well as poor service utilization.
Emergency transport interventions could save an estimated 75percent of pregnant women each year, which could further savenearly 14,500 births if functional referral systems are put in place.
The study will be conducted in the Millennium Village project communities in the Ashanti Region of Ghana. A literature review will be done. Data on ambulance utilisation for maternal emergency referral in the health facilities in this communities will be accessed. Other case received without intervention of the ambulance services within the same period will also be collected .The response times and cost will be determined as well as the outcomes of the different scenarios. Analysis will then be done to assess the impacts.
Lack of ambulances and absence of other means of transportin remote areas (Shehu et al. 1997) and high transport costsrepresent a major constraint for women and their familieswho need to access health facilities for both preventive andemergency care. A key solution therefore is to improvetransport access in a way that is both affordable andsustainable for these two levels of care.
It should be possible to reduce maternal deaths in rural Ghanaby effective and efficient emergency (ambulance) referraltransport planning .
1. Babinard,J. and Roberts,P.,2006 Maternal and Child Mortality Development Goals: What Can the Transport Sector Do? The World Bank Group Washington, D.C. http://www.worldbank.org/transport/
2. Thaddeus S, Maine D (1994) Too far to walk: maternal mortality in context. Soc ScMed 38(8): 1091–1110.
3. Lungu K, Kamfose V, Hussein J, Ashwood‐Smith H (2001) Are bicycle ambulances and community transport plans effective in strengthening obstetric referral systems in Southern Malawi. Malawi Med J 13: 16–18.
4. Maxwell Ayindenaba Dalaba,et al.,2015 Cost to households in treating maternal complications in northern Ghana: a cross sectional study. BMC Health Services Research 2015, 15:34 doi:10.1186/s12913‐014‐0659‐1
5. Murray SF, Pearson SC (2006) Maternity referral systems in developing countries: current knowledge and future research needs. Soc Sc Med 62: 2205–2215.
6. WHO | Maternal mortality [http://www.who.int/mediacentre/factsheets/fs348/en/]
Without intervention
With intervention
UNIVERSITY OF LEEDS
`
Printing:Utilizing Real Time Bus Information Technology
To Encourage Bus Travel
Student: Steven Lightfoot (email: [email protected]), Supervisors: Jeremy Toner and Mark Wardman
Background
• Metro Tracker survey 2014, Vector research
• Mishalani, Rabi G., Sungjoon Lee, and Mark R. McCord. 2000. "Evaluating real-time bus arrival information systems." Transportation Research Record: Journal of the Transportation Research Board 1731.1: 81-87.
• Moss S 2015. The Guardian website. Available from: http://www.theguardian.com/cities/2015/apr/28/end-of-the-car-age-how-cities-outgrew-the-automobile
• Tang, Lei, and Piyushimita Vonu Thakuriah. 2012 "Ridership effects of real-time bus information system: A case study in the City of Chicago." Transportation Research Part C: Emerging Technologies 22: 146-161.
• Transportation Research Part A: Policy and Practice, Volume 45, Issue 8. 2011, Pages 839–848
• Transportation Research Part C: Emerging Technologies. Volume 53. 2015, Pages 59–75
• TLP Projects – Monitoring Report 2009 to 2013, Metro 2013
• Traveline. 2015. (online). Available from: http://dashboard.mxdata.co.uk/traveline/Account/login.aspx
Objectives
• New technologies enabling the provision of real time bus information and the growth in smartphone use have the potential to transform the way people view bus travel options.
• Utilize real time information to improve the way bus information is presented to the public.
• Set out best way of displaying real time information to public on stop displays, computers and mobile phones.
• Maximize public access to, awareness and usage of real time information.
• Set out best practice and future developments that will show how real time information can be utilized by bus operators and traffic control centers to improve reliability and speed whilst reducing operating costs.
Data and Scope
• Real time systems and literature from across the world will be reviewed.
• Data sources include: transport press, West Yorkshire bus user survey, public usage of real time outputs in Yorkshire, real time user groups etc.
• Focus for recommendations will be Yorkshire, however they will be able to be adapted for other areas.
• Recommendations will aim to retain existing bus users and attract new users.
• Recommendations will focus on existing bus regulation system in Yorkshire, but will consider different regulation models.
• Risks include:
• Difficulty accessing commercially sensitive formulas used to generate real time predictions.
• Lack of regulation meaning there is no central body able to ensure recommendations are implemented.
Methodology
• Result 1
• Result 2
• Result 3
Initial Findings
References
• Provision of real time bus information can increase bus usage.
• Can reduce both actual wait time and perceived wait time
• ‘Digital information is the fuel of mobility’,
• ‘Information about mobility is 50% of mobility’
• Large increase in real time mobile apps availability and usage facilitated by open data provision.
• First and Google apps dominate Yorkshire market with 88% market share.
• 290% increase in real time mobile app usage in last 6 months in West Yorkshire.
• More modest increase in internet usage and a fall in text usage.
• Awareness of real time mobile internet and apps still relatively low at 27% in West Yorkshire.
OBJECTIVE 1 – PRESENTATION
OBJECTIVE 2 – ACCESS AND USAGE
OJECTIVE 3 – SPEED, RELIABILITY AND COST
• Real time bus information utilizes satellites to track bus locations. This enables accurate arrival times bus to be shown to the travelling public, instead of just timetable information.
• Real time bus systems have been introduced in major transport areas across the world.
• Difficulty accessing and using bus information has historically been a significant barrier to encouraging sustainable travel behaviour.
• Real time information can be shown on mobile phones. Mobile phone usage is increasing across the world. The proportion of people in West Yorkshire with a mobile phone has increased from 90.3% in 2012 to 93% in 2014.
• Bus usage is falling in West Yorkshire. The proportion of people using a bus monthly has fallen from 57.1% in 2011 to 52.4% in 2014.
• Technological advances have improved the practicality and reduced the cost of real time bus information systems.
• Real time bus technologies present new opportunities for improving bus reliability through linked technology.
• Including Traffic light bus priority and improved scheduling.
• The output from real time can be used to improve bus services.
• Operators in Yorkshire analyze past performance to improve scheduling. This can increase reliability and reduce operator costs.
• Link to Yorkshire traffic control centers can give traffic light priority to buses. This can increase reliability and reduce journey time and operator costs.
• Introduction of bus traffic light priority to 200 junctions in West Yorkshire was shown to have a Benefit:cost ratio of 8.
Evaluation of the Influence on Driving Behaviour by Music Tempo
Data Collection
• Free driving task
1. Average, maximum, minimum driving speeds
2. Average, maximum lateral deviations
• Overtaking task
1. Maximum speeds
2. Minimum headway distances before and after overtaking
• Approaching signlised junction task
1. Decision making
2. Violation frequency
3. Passing speeds
• Stopping task
1. Reaction time
Objectives
The study will be approached through drivingsimulator. Four questions are aiming to beanswered in this research about lisening slow/fasttempo music during driving:
1. How much degree of influences on drivingperformance under free driving condition?
2. Does the music induce more dangerous drivingin overtaking process?
3. Will the drivers be more aggressive towards asignalised junction?
4. Is there any deterioration in reaction time for anemergency stop?
Background
Dibben and Williamson (2007) conducts a surveyand finds that 75% young drivers listen musicduring driving. However, the young drivers, whopreferred no music driving environment, are lessinvolved in road accidents.
The study in Brodsky(2001) selects some fasttempo music to test the driving performance.Higher driving speed, and more frequent trafficviolations are shown. Fast-paced music is provedto deteriorate the driving behaviour.
In most of the previous studies ,drivers are testedby driving in a city through driving simulator, butnot in some specific critical conditions. In currentstudy, some specific scenarios will be set up inorder to thoroughly investigate the drivinginfluence on these conditions, for example,overtaking, dilemma in signalised junction, andemergency stop.
Waterhouse et al., (2010) mentions that apart fromtempo, lyrics, melody, loudness and otherparticular circumstance can also affect the musicaltaste. To reduce the variables, same set of musictracks, which differed in tempo, are used in thisstudy.
Tasks in a testExperimental Designs
20 driving licence owners, who age from 20-30years old, will be invited to parcitipate theexperiment, because they are the most frequentgroup of listening music, as well as the highestrisk group of getting involved in accidents.Experimental flow is below:
Briefing (15mins)
• Introduce about the experiment, including all thetasks they will meet in the test.
• Explain the manipulation of driving simulator.• Provide free driving section for familiarisation.
Testing (55mins)
• Without music, fast tempo and slow temposcenario tests will be finished by participantsrespectively in random order.
Surveying (10mins)
• Complete a self-reflection questionnaire• Personal information: age, gender, driving
experience, etc.• Personal perception in slow and fast tempo
music for each individual task• Any mistake has taken in the test.
Driving Simulator
Overtaking
Approaching
signalised
junction
Stop
immediately
and restart
Overtaking
Start to play
slow/fast tempo
music
Approaching
signalised junction
Stop immediately
and finish
Free Driving
for 10 minutes
at 60mph
Free Driving
for 10 minutes
at 60 mph
2 mins 2 mins
2 mins2 mins
Data Analysis and Expected Results
Three sets of dependent variable data comparisons will be analysed:
• Without music VS Slow tempo music
• Without music VS Fast tempo music
• Slow tempo music VS Fast tempo music
The results from the fast tempo music are expectedto show:
• higher free driving speeds,
• dangerous overtaking behaviour, with higher speeds and shorter headway distances
• tending to pass the signalised junction with higher speed rather than decide to stop in dilemma situation,
• and a longer reaction time.
Li Shaotang, Alvis Email: [email protected] Msc (Eng) Transport Planning and Engineering Supervisor: Dr. Daryl Hibberd
Motorcyclists’ Acceptance of Automated
Road Transport Systems in Taiwan Shu-Cheng, Hsieh ([email protected]) MSc (Eng) Transport Planning & Engineering Supervisor: Dr Natasha Merat, Tyron Louw
Motorcycles in Taiwan Large density and amount of registered motorcycles Motorcycles : Other vehicles = 1.8 : 1
Public Transport in Taiwan Projects promoting public transport
by Taiwanese government since 2010 Involving buying new buses, improving
service quality, providing real-time and subsiding rural routes
2. Background
Year Car Bus & Coach
LGV HGV Subtotal Motorcycle
2013 6,236,879 31,960 875,544 162,122 7,367,522 14,195,123
2014 6,405,778 32,928 890,703 163,446 7,554,319 13,735,994
Road user interactions Conflicts between
motorcycles and buses (Particularly at bus stops)
Public Transport Little changes on usage Financial difficulties for
operators Lack of drivers
3. Research Problems
An example: City Mobil2 An EU project
assessing ARTS Deliver ARTS in several
European cities Investigate road users acceptance
(focus on pedestrian) Aims: 1) Evaluate what ARTS could provide to sustainable
transport 2) Examine and improve interactions between ARTS and
other road users
How about in Taiwanese transport environment?
4. Research Motivation
Literature review, technology approach and integration Questionnaire Sample: Motorcyclists in Taiwan Asking acceptance in two sections
Data Analysis and discussion
5. Methodology
Section 1
• Applying Drive Behaviour Questionnaire
• Initial acceptance by introducing ARTS
Section 2
• Scenario with safety systems on ARTS
• Scenario with road infrastructure for ARTS
Understand the factors that influence motorcyclist’s acceptance of ARTS
Motorcyclist–centred design recommendation fro ARTS in Taiwan
6. Expected Outcomes
Public transport systems based on the use of a fleet of communication-enabled cybercars – road vehicles with automated driving capabilities.
Advantages Provide “Last-mile connections” for individuals Low personnel costs (No drivers) Sustainable urban transport
Existing Cases
1. What is Automated Road Transport Systems (ARTS)?
ARTS in the West Region of Lausanne, Switzerland
ARTS in La Rochelle, France
Key References CityMobil (2015), http://www.citymobil-project.eu/. CityMobil2 (2015), http://www.citymobil2.eu/ Directorate General of Highways, Ministry of Transportation and Communications, Taiwan (R.O.C.) (2014), Annual Report for Motor Vehicle Administration. Rockall, Wil, 2014, Can driverless car see off cyber attacks? [Online] London, United Kingdom. http://goo.gl/oFQZNg Reason, Manstead, Stradling, Baxter & Campbell (1990), Errors and violations on the roads: a real distinction? http://goo.gl/ZMzgVX
Understand motorcyclists’ initial acceptance of ARTS in Taiwan
Find out what will increase motorcyclists’ acceptance and confidence of ARTS when assessing them, in: Safety systems on ARTS Road infrastructure
5. Objectives
Can ARTS be a solution?
ROLE OF PRIVATE FINANCE IN AIRPORT DEVELOPMENTNAME: SAMUEL APPIAH ADJEI
EMAIL: [email protected] INDEX: 200872578SUPERVISOR: PROF. NIGEL SMITH
BACKGROUND AIM METHODOLOGY
1. The fundamental change in the airport industryoccurred after the 1986 Airports Act which was tointroduce the privatization and commercializationinto the aviation sector2. There exist different ownership models afterthe introduction of Airport Act3. Most airports in the UK has experienceddifferent ownership types over the years4. Some of the ownership types include purelypublic airport, public private partnership andpurely private ownership5. Research would undertake time series analysisof effects of ownership change on airportspassenger trends
Subhead• Volor ilit nonsendiate magna ad erciliquip eugiam velent alisl dolor auguerat. Utdolore consendrerit verilla cons nosto cons nim quis elisci ex ea commy nis doloreetex estie vent ad molesto diat.• Volor ilit nonsendiate magna ad erciliquip eugiam velent alisl dolor auguerat. Utdolore consendrerit verilla cons nosto cons nim quis elisci ex ea commy nis doloreetex estie vent ad molesto diat.•Volor ilit nonsendiate magna ad erciliquip eugiam velent alisl dolor auguerat. Utdolore consendrerit verilla cons nosto cons nim quis elisci ex ea commy nis doloreetex.
This research primarily examines the impactof airport ownership type on airportefficiency
1. Analyse airport passenger trendsbetween 2000 and 20142. Analyse airport freight trends between2000 and 20143.Identify impact of airport ownership typeon passenger trends4. Identify measures to improving airportpassenger and freight growth
Leeds Bradford Airport
FURTHER WORK
1. Study effects of various airport serviceson passenger numbers2. Evaluate private finance on airportdevelopment
1. The fundamental change in the airportindustry occurred after the 1986 Airports Actwhich was to introduce the privatization andcommercialization into the aviation sector2. There exist different ownership models afterthe introduction of Airport Act3. Most airports in the UK has experienceddifferent ownership types over the years4. Some of the ownership types include purelypublic airport, public private partnership andpurely private ownership5. Research would undertake time series analysisof effects of ownership change on airportspassenger trends
UK AIRPORT UK OWNERSHIP
PRIVATIZED AIRPORTS
PUBLIC PRIVATE
PARTNERSHIP
PUBLIC AIRPORTS
OBJECTIVES
CASE STUDY
TIME SERIES ANALYSIS
PASSENGER TRENDS FREIGHT TRENDS
DATA COLLECTION
AIRPORT ANNUAL REPORT CIVIL AVIATION AUTHORITY
CASE STUDY APPROACH
LEEDS BRADFORD AIRPORT
Public Airport
2000-2007
Privatized 2007 to
Date
REFERENCES
Butcher L. (2014), Aviation: Regional Airports House of Commons,House of Commons LibraryOxford Economics (2011) Economic benefits of air transport in the UKYin, R. K. (2014) Case Study Research. 5th Edition. California. SagePublications Inc.
Traffic flows thresholds for Shared Space in Leeds
Transport Planning and Engineering. Student: Russell Oakes Supervisor: Dr James Tate
IntroductionShared Space is a concept where streets are re-engineered to reduce the dominance of motor vehicles (DfT, 2014) Street signage is limited and kerb heights are reduced or removed completely in some cases.
The focus for this project will be in Headingley, North West Leeds. Pedestrian desire lines vary due to the mixture of retail and leisure activities this district has to offer; therefore providing an ideal location to test the theory.
Literature The dissertation will be educated by various sources of literature, including...
Manual for Streets (1 & 2), Liverpool City Centre Plan (1961), Living Streets Policy, Local Authority Policy (for example Leeds City Council Supplementary Planning Documents), University of the West of England Research, Written interview with the late Hans Monderman and scheme studies.
Objectives
To ascertain the potential for bringing Shared Space to Headingley by:
Understanding previous comparable Shared Space Schemes
Compiling a resource containing pedestrian and vehicular data
Applying the data to a Micro-simulation Package (Aimsun & Legion) with sensitivity tests
Analysing the Aimsun & Legion outputs
Determining applicability to Headingley and wider Leeds
These objectives will act as milestones throughout the dissertation with the expectation that each objective will be a development on its predecessor.
Methodology
The project will require site visits to various contrasting examples, compilation of pedestrian data from Leeds City Council and a suitable model simulation running to satisfy the scope of the project.
Anticipated issues include the inability to compile pedestrian data for the Headingley area, therefore flexibility with pilot site locations may be required.
Two preliminary pilot sites of contrasting traffic density will be used in order to determine the relative scales of operation for a Shared Space scheme. Currently, these sites are North Lane/Otley Road and St Michaels Road outside the Church.
Coventry Top: Before. Source: Oakes, R (2010) Bottom: After. Source: Oakes, R (2015)
PrestonDe-cluttering of street furniture including the removal of traffic lights
Narrowing of Fishergate providing wider pavements
Provision of informal pedestrian crossings
Top: Before. Source: Oakes, R (2013) Bottom: After. Source: Oakes, R (2015)
Urb
an
Ca
se S
tud
ies
Top: Before. Source: Oakes, R (2010) Bottom: After. Source: Oakes, R (2015)
Downgraded routes complimented by extensive landscaping
Closure of through routes and implementation of UKs largest 20mph zone (Coventry City Council, 2010)
Mixture of zebra and informal crossings
Coventry
Top: St Michaels Road. Source: Oakes, R (2015) Bottom: Otley Road/North Lane. Source: Oakes, R (2015)
Sources used in the dissertation will include:
Early Indications and Potential Outcomes
No Shared Space scheme is identical, as demonstrated with the case studies. Therefore, the site visits will assist in the appreciation of the issues apparent in Headingley.
An understanding of which environments would best suit a Shared Space scheme with the potential to apply the key findings of this project to policy making within Leeds City Council.
Communities are aware of the Shared Space concept and have approached Leeds City Council requesting that this is investigated.
If the timescales fit, efforts will be made to draw comparisons where relevant to other Shared Space schemes. This may lead to good practice workings with other Local Authorities subject to interest.
Sources of Information
The dissertation will call upon quantitative and qualitative sources in order to provide a robust analysis. This could include:
Quantitative
Leeds City Council Transport Monitoring Database
Primary data collection (where required)
Scheme monitoring reports (where available)
QualitativeCity and County Borough of Liverpool 1965, Liverpool City Centre Plan, Liverpool, City and County Borough of Liverpool
Moody, S. and Melia, S. (2014) Shared space: Research, policy andproblems. Proceedings of the Institution of Civil Engineers - Transport, [Online] Available at: http://www.icevirtuallibrary.com/content/article/10.1680/tran.12.00047 (Accessed 23rd April 2015)
Meta-analysis of electric vehicles’ range predictionEU in attempt to invest in innovation in Europe and also to improve the life quality of theunion’s citizens, introduced the programme “HORIZON 2020” for 2014-15; where one of themain goals is “smart, green and integrated transport”. A key topic of this programme is theimprovement of “green” electric vehicles’ technology and charging infrastructure; in anattempt to make electric vehicles (EV) prevail in the vehicle market, as a “cleaner” technology,improving urban air-quality, and also to improve the driving experience of EV drivers.According to the last, this report aim to investigate the prediction of the driving range of EV;which is connected with the real-time information (digital support) for EV drivers for bettertrip planning and access to charging facilities.
The main objective of this research is toinvestigate the parameters that affect the drivingrange of EV in real-life driving conditions, inorder to test and evaluate the accuracy of theexisting methods currently used for EV drivingrange prediction. This aim to help drivers predictthe residual driving range of their vehicles inorder to improve their driving experience andbetter estimate their trips.
(a) a smart grip giving information to the EV driver on when the next charge is required and the near available chargingfacilities, through GPS positioning. This could be through a mobile application or on a pre-installed application on thevehicle. (b) an example of application (“Next Charge”, android app) giving information to the driver about the available nearcharging stations. (c) an application (“EV Range”, android app) for route planning by the driver; with inputs origin anddestination, vehicle model and passengers number, and outputs distance, time, consumption (Wh/km), percentage of thebattery capacity left (%), and driving range (km) for all possible routes.
• Develop the framework, the modelling of the motor’s required power based on travel ( i.e. distance, traffic conditions, slope, etc.), vehicle (i.e.weight) and driver (i.e. aggressiveness, route choice etc.) related parameters and battery’s discharge rate validation based on the battery'sspecifications: capacity, state of charge (SOC), current (I), voltage(V), etc.
• Investigate the applied and researched modelling methods (for both the motor and the battery) and related parameters• Evaluate the validity and transferability of the methods and the findings regarding how the data where collected, by which conditions, the data
sample size, etc.• Research transferable methodology from other studies that can be examine for EVs i.e. ICE vehicles fuel consumption and emissions factors• Define the parameters into modelling factors and discuss limitations• Make the a comparison of the methods and give the proposed method or combination and make proposals for improvements and further
research
M e t h o d o l o g y
O b j e c t i v e
Can driving range be predicted accurately? Which data are required? Is theuse of these models in real-life feasible?
P a r a m e t e r sOne of the most advanced features of an EV, compared to the conventional ICE vehicle is its ability toregenerate electricity when decelerate through the regenerative braking system (RBS).
Power (KW)/ Acceleration (m/s2) Figure (c) and (d)• For acceleration between -1.5 and 1.5 m/s2, the power proportionally increases with the increase of the
acceleration.• For acceleration bellow -1.5 m/s2 or above 1.5 m/s2 the power remains almost the same and doesn’t change with
the acceleration.• For both urban (in-city) driving and freeway driving, the power lies between -5 kW and 20 kW• The lower bound is low because EV’s regeneration is limited by the battery pack’s ability to accept charge which is
controlled by the battery management system (BMS).
Power (KW)/ Roadway gradient (%) Figure (e) and (f) (Gradient information was collected from Google Earth)• As the gradient is increasing the required power is increasing too.• The change in power is significantly larger when the grade is positive• For urban (in-city) driving, the change in power is 20 kW (5 - 25 kW) when the grade changes from 0 to 6%; but
when the grade changes from -6 to 0% the power increases only 5 kW (0 - 5 kW)• For the freeway driving, the needed power changes from 12 to 32 kW (20 kW difference) when the grade changes
from 0 to 6%; but when the grade changes from -6 to 0% the power increases only 7 kW (5- 12 kW).• For the same gradient the freeway driving requires more power than urban driving probable due to higher speeds.
The huge potential benefits of EVs have already attractedsignificant interest and investment in EV technology. Since2010 more than 20 manufactures introduced EVs.
(a) (b) (c)
Reference: Wu, X., Freese, D., Cabrera, A., & Kitch, W. (2015). Electric vehicles' energy consumption measurement and estimation. Elsevier, Transport Research Part D, 52-67.
Collection of traffic condition and road type data
Categorisation of road-type and congestion level
collect vehicle response to traffic and road conditions
Simulate vehicle response to traffic and road conditions
Model development
1. Single vehicle driving cycle
2. Multiple vehicles driving cycle
1. Road type2. Speed3. Speed/stops-starts4. Speed/ acceleration-
deceleration5. LOS
1. Data logger on EV & GPS positioning (road-information from interactive maps)
2. Data logger on non-EV vehicle(s)3. Data logger on non-EV vehicle(s) & GPS
positioning4. Aggregate average data (pre-developed
driving cycle used)
1. Neural Network2. Simple statistical analysis
1. Data analysis based algorithms2. Data analysis based & physic based
approach algorithms3. Physic based approach algorithms,
static model (use data for validation)
3. Dynamometer driving schedule
1. Speed2. Speed/stops-starts3. Speed/ acceleration-
deceleration
1. Data logger on EV2. Data logger non-EV vehicle3. Aggregate average data (pre-developed
driving cycle used)
1. Statistical analysis 1. Data analysis based algorithms2. Physic based approach algorithms,
static model (use data for validation)
4. Derive from traffic model 1. Road type-traffic model 1. Aggregate average data 1. Statistical analysis 1. Data analysis based & physic based approach algorithms
F r a m e w o r k o f p u b l i s h e d E V r a n g e p r e d i c t i n g m e t h o d s
Who spend what on the High Street? A comparison of the importance of non-car access between city
centre and local shops areas. Institute for Transport Studies, University of Leeds, UK.
RESEARCH QUESTION
(sustrans, 2006)
55% 22% (4'. •"'10% (6cq13% (1 1 %)
Actual mode of customer travel(Shopkeepers estimates in brackets)
Shoppers' choice of travel modes in Bristol study
rivers l imit the range of
compact urban centre, flat
ervice.
One of the best Park and Ride
QUESTIONS & OBJECTIVES
Which are the accessibility patterns in the city centre and
local shops areas?
Have they an implication in shops turnover?
To determinate if retailers perception about their customer mode
of access is accurate, in order to promote a better understanding of
transport and land-use policies.
CITY OF YORK
Advantages for sustainable modes:
geography and good public transport s
Foot street historical and retail centre.
schemes in the UK.
Disadvantages: Historical walls and
interventions.
METHODOLOGY
1) Literature review of economic, planning and transport
approaches to High Streets in U.K. and previous academic and "grey"
studies.
2)Questionnaires designing based on previous studies and
amendments for accuracy to City of York
RISK
Data collection task may take more time than expected.
Get retailers answers while they are working.
Fail in achieving the proposed sample size.
Lack of support from York City Council
CONTEXT
"Local areas should implement free controlled parking schemes..."
"Cars are an intrinsic part of the way many people shop..."
Worths Report,2011,p.5 and p.271
"There is not such thing as "free" parking"
(Tyler et a1,2012,p.651
"The literature on parking and retail divides into two groups: those
suggesting that parking is important for retail activity and those arguing
that retailers have a wrong perception about the modal split of their
customer and usually overestimate car use for shopping"
IMingrado,2012,p1951
3)Data will be collected by different methods with the aim of
accumulating as many answers as possible: face to face, mail drop and
email questionnaires.
4. Analysis: data analysis, interpretations and comparison with other
results from UK and overseas.
1. Conclusion: Findings of the work. Answers to the research questions
and implication for the city of York.
REFERENCES
s,M. (2011). The Portas Review. An independent review into the future of our Highs Streets. [ONLINE].
[Accessed 29 February 2015]. Available from: https://www.gov.uk/government/uploads/system/up-
loadsiattachment_dataifile/6292/2081646.pdf Sustrans. (2006). Real and Perceived travel behaviour in
neighbourhood shopping areas in Bristol. Bristol: Sustrans.
Tyler, S., Semper, G., Guest, P., & Fieldhouse, B. (2012). The relevance of parking in the
success of urban centres, A review for London Councils.
UNIVERSITY OF LEEDS
DATA
Desirable sample size:
Consumers centre(n=200)
Consumer local( n= 100);
Retailers centre(n= 50)
Retailers local(n=25).
QUESTION EXAMPLES
"How often and by which means do
you shop"?
"How often and by which means
do you think your customers
shop?"
Poster Presentation: 01 May 2015. Student: Pedro Scarpinelli . Dissertation Tutor: Professor Greg Marsden. Institute for Transport Studies, University of Leeds
Traffic flows thresholds for Shared Space in Leeds
Transport Planning and Engineering. Student: Russell Oakes Supervisor: Dr James Tate
IntroductionShared Space is a concept where streets are re-engineered to reduce the dominance of motor vehicles (DfT, 2014) Street signage is limited and kerb heights are reduced or removed completely in some cases.
The focus for this project will be in Headingley, North West Leeds. Pedestrian desire lines vary due to the mixture of retail and leisure activities this district has to offer; therefore providing an ideal location to test the theory.
Literature The dissertation will be educated by various sources of literature, including...
Manual for Streets (1 & 2), Liverpool City Centre Plan (1961), Living Streets Policy, Local Authority Policy (for example Leeds City Council Supplementary Planning Documents), University of the West of England Research, Written interview with the late Hans Monderman and scheme studies.
Objectives
To ascertain the potential for bringing Shared Space to Headingley by:
Understanding previous comparable Shared Space Schemes
Compiling a resource containing pedestrian and vehicular data
Applying the data to a Micro-simulation Package (Aimsun & Legion) with sensitivity tests
Analysing the Aimsun & Legion outputs
Determining applicability to Headingley and wider Leeds
These objectives will act as milestones throughout the dissertation with the expectation that each objective will be a development on its predecessor.
Methodology
The project will require site visits to various contrasting examples, compilation of pedestrian data from Leeds City Council and a suitable model simulation running to satisfy the scope of the project.
Anticipated issues include the inability to compile pedestrian data for the Headingley area, therefore flexibility with pilot site locations may be required.
Two preliminary pilot sites of contrasting traffic density will be used in order to determine the relative scales of operation for a Shared Space scheme. Currently, these sites are North Lane/Otley Road and St Michaels Road outside the Church.
Coventry Top: Before. Source: Oakes, R (2010) Bottom: After. Source: Oakes, R (2015)
PrestonDe-cluttering of street furniture including the removal of traffic lights
Narrowing of Fishergate providing wider pavements
Provision of informal pedestrian crossings
Top: Before. Source: Oakes, R (2013) Bottom: After. Source: Oakes, R (2015)
Urb
an
Ca
se S
tud
ies
Top: Before. Source: Oakes, R (2010) Bottom: After. Source: Oakes, R (2015)
Downgraded routes complimented by extensive landscaping
Closure of through routes and implementation of UKs largest 20mph zone (Coventry City Council, 2010)
Mixture of zebra and informal crossings
Coventry
Top: St Michaels Road. Source: Oakes, R (2015) Bottom: Otley Road/North Lane. Source: Oakes, R (2015)
Sources used in the dissertation will include:
Early Indications and Potential Outcomes
No Shared Space scheme is identical, as demonstrated with the case studies. Therefore, the site visits will assist in the appreciation of the issues apparent in Headingley.
An understanding of which environments would best suit a Shared Space scheme with the potential to apply the key findings of this project to policy making within Leeds City Council.
Communities are aware of the Shared Space concept and have approached Leeds City Council requesting that this is investigated.
If the timescales fit, efforts will be made to draw comparisons where relevant to other Shared Space schemes. This may lead to good practice workings with other Local Authorities subject to interest.
Sources of Information
The dissertation will call upon quantitative and qualitative sources in order to provide a robust analysis. This could include:
Quantitative
Leeds City Council Transport Monitoring Database
Primary data collection (where required)
Scheme monitoring reports (where available)
QualitativeCity and County Borough of Liverpool 1965, Liverpool City Centre Plan, Liverpool, City and County Borough of Liverpool
Moody, S. and Melia, S. (2014) Shared space: Research, policy andproblems. Proceedings of the Institution of Civil Engineers - Transport, [Online] Available at: http://www.icevirtuallibrary.com/content/article/10.1680/tran.12.00047 (Accessed 23rd April 2015)
Evaluating the efficiency of Network Aggregation in providing accurate results, using SATURN software. A case study of the Lendal bridge closure in York City. Panagiotis Anastasiadis
Dr. David Milne (Supervisor), Prof. David Watling (2nd reader)
I. Understand the patterns and unique characteristics of York’s network
II. Investigate suitable approaches to network simplification III. Define and describe step by step a network simplification
method, which best represents the effects of the traffic. IV. Identify the ideal level of simplification to provide adequately
accurate results that help in evaluating transport policies.
3. Case study
Lendal bridge closure trial for cars, lorries and motorbikes (10:30-17:00). Start date: 27 August 2013 End date: 26 February 2014
5. Methodology (Link extraction proposed methods)
4. Objectives
I)
II)
Adeke, Paul Terkumbur │ Supervisor: Dr. Richard Connors │ 2nd Supervisor: Prof. Stephane Hess
Objectives of the study include;
To evaluate performance characteristics of different priority queuing systems for
economic and efficient service delivery.
To implement the model using MATLAB – SimEvent based on real-life situations.
To propose best configurations and service protocol for efficient and economic
operations of a security check system of an airport.
System Model Structure
Arrivals described as Poisson (Markovian) Process
Queue Discipline; FIFO and Non-Preemptive process
Constant Arrival Rate; λt = λn + λp
Constant Departure Rate; µt = µn + µp
Number of servers; Nt = Nn + Np
Waiting Times for NQ and PQ; Wn & Wp
Queue Lengths for NQ and PQ; Ln & Lp
Deterministic service time
Steady state system ie ρn + ρp < 1 ρ = λ/µ
Queuing Area Service Area
λt µt
Nn
Np
µn
µp
Ln
Wn
Lp
Wp
Priority Queue
Normal Queue
Schematic diagram of priority queue
Discrete Random Arrivals (Poisson Process)
Queue Choice - Binary Logit Model
Arrivals on PQ Arrivals on NQ
Evaluation of NQ Performance
Departures out of System
Departures Departures
The study aims at developing a mathematical model use for cost-benefit-analysis of
airport security checking system based on service protocol, queue performance and
configuration of a priority queuing system measured by time-money value of arriving
customers.
Parameters and Basic Assumptions:
Mathematical Models developed in the past for examining the performance of
priority queues potentially include; the state-reduction based variant by Kao (1991),
modified boundary algorithm by Latouche (1993) and logarithm reduction algorithm
model by Latouche and Ramaswamni (1993) (Kao and Wilson ,1998)
Previous studies examined suitable configurations (number of servers) and protocols
(discipline) for priority queues with stochastic (random) arrivals, infinite or infinite
capacity and exponentially distributed service times; ranging from one server to
multiple servers with varied classes of priorities (Gail, et. al., 1988; Osogami et. al.,
2003; Harchol-Balter, et. al. 2005;)
The significant impact of system configuration, protocol and discipline to the
performance of priority queuing systems have been examined by previous researches
(Osogani, 2003; Harchol-Balter, 2005).
A priority queueing system is that in which arrivals are classified into groups based on
criterion. Though subjective and varies from one individual to another, time-money
value for every individual influences their respective decisions. Benjamin Franklin
once said ‘Time is Money’. In a queuing system, time-money value of arrivals is
essential and can be used to categorise customers into separate channels aimed at
optimum service delivery. This study considers Normal Queue (NQ)–without extra pay
and Priority Queue (PQ) - with extra pay in a security checking system of an airport.
NQ
& P
Q
Customers on NQ allowed to switch
to PQ without extra pay in the absence
of priority customers
Scenarios Probability Generating Function for Poisson
Arrivals
Develop a Binary Logit Model use for
Splitting arrivals into NQ and PQ based on
time-money value
Formulation of system operation
protocol/configuration and assumptions
Debugging of Simulated Model
Model Simulation using MATLAB (SimEvents)
Build performance evaluation model for
NQ & PQ using probabilistic theorems
and Matrix algebra
Calibration and Validation of Model Using real-life data
Cost-Benefit Analysis based on system
performance parameters
Comparative Analysis of Scenarios using
statistical techniques
Gail, H. R., Hantler, S. L. and Taylor, B. A. 1988. Analysis of a Non-Preemptive Priority Multiserver
Queue, Advances in Applied Probability, Applied Probability Trust, Vol. 20, No. 4, pp. 852-879.
Harchol-Balter, M., Osogami, T., Scheller-Wolf, A. and Wierman, A. 2005. Multiserver Queueing Systems with
Multiple Priorities, Queuing Systems: Theory and Applications Journal (QUESTA), 51, 3-4, 331 – 360.
Kao, E. P. C. and Wilson, S. D. 1998. Analysis of Nonpreemptive Priority Queues with Multiple Servers and Two
Priority Classes, European Journal of Operational Research 118 (1999)181– 193.
Osogami, T. 2003. How many Servers are Best in a Dual-priority FCFS System? Technical Report,
School of Computer Science, Carnegie Mellon University.
Customers on NQ not allowed to
switch to PQ in the absence of priority customers-Priority servers kept idle.
Institute for Transport Studies
Evaluation of PQ Performance
UNIVERSITY OF LEEDS
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
Nu
mb
er
of
Pas
sen
gers
(P
ers
on
s)
Time Period (min.)
Arrival Curve
Departure Curve
WX
LX
PERFORMANCE CHARACTERISTICS OF A QUEUE (X)
∞
∞
Other parameters of interest include max. number of passengers in the system when arrival and departure rates are not constant and idle period; at low, medium and high demand.
01/05/2015
Optimum service protocol
Optimum system configuration
Optimum service Time per customer
Optimum service charge per customer
MATHEMATICAL MODELLING OF PRIORITY QUEUES
TRANSPORT AND CITY COMPETITVNESS
Do Transport Investments Matter More than Lower Taxes?
Dissertation for M.Sc. (Eng) in Transport Planning and Engineering By Nalbi Sadek, B. Sc., Supervisors: Caroline Mullen & James Laird September 2015
BACKGROUND & MOTIVATIONS With scarce resources, limited budgets and continued emphasis on economic development and growth; how do local governments go about implementing their economic development policies?
Is transport infrastructure investment the beating heart of economic redevelopment?
To what extent do fiscal policies influence business attitudes and decisions (location and investment choices)?
Why focus on Cities? Do the City Competitiveness rankings
matter? Should Cities be pursuing City Competitiveness superiority?
OBJECTIVES Identify an operational definition for
City Competitiveness. Review the factors (policy tools)
promoting economic growth & development and their degree of importance
How do local governments pursue City Competitiveness (using case studies) in comparison to academic theory
UNIVERSITY OF LEEDS
Institute for Transport Studies
METHODOLOGY
•Formulate Research Questions
Literature Review
•Review Case Studies (Greater Manchester and Leeds)
•Stakeholders Interviews
•Targeted Literature Review
Problem Solving
•Future Work Recommendations
Conclusions
CONCLUSIONS: Which factors to pursue first are
dependent on the unique characteristics of a city
There are fundamental characteristics needed for economic development and hence are universally applicable
Raising government funds and government investments are an interactive cycle rather than conflicting objectives
Future Work RECOMMENDATION Investigate how city competiveness is
perceived in developing countries
1. Context In recent years the Government of Uganda has
concentrated on road infrastructure investment. There is need to assess the extent to which it has impacted
on the local economy.
In recent years the Government of Uganda hasconcentrated on road infrastructure investment.
There is need to assess the extent to which it has impactedon the local economy.
ROLE OF TRANSPORT IN PROMOTING ECONOMIC DEVELOPMENT IN UGANDA;‐A Case Study Along the Corridors of Gulu to Atiak.
2. Research Objective To identify the direct impacts of transport investment in
terms of changes in petty trade and journey attributes alongGulu to Atiak corridor.
To identify the direct impacts of transport investment interms of changes in petty trade and journey attributes alongGulu to Atiak corridor.
4. Methodology
3. Research Questions To what extent have there been changes in modes of
transport that are owned and used for mobility as aconsequence of transport investment?
How does transport infrastructure investment affect the levelof petty trade?
To What extent has travel time and cost changed?
To what extent have there been changes in modes oftransport that are owned and used for mobility as aconsequence of transport investment?
How does transport infrastructure investment affect the levelof petty trade?
To What extent has travel time and cost changed?
‐ Primary sources‐ Questionnaire Design& Administration
Traders & Local Residence
In Area With Project In Area Without Project
Statistical Analysis of data
Secondary sources
Results
Compareinformation and Draw conclusions
Data sources and Uses
Can’t tell how truthful a respondent is being. Cant tell how much thought a respondent has put in. Respondents get Exhausted leading to bias responses systematic bias by enumerators
5. Risk involved
6. Key Points from Pilot‐
‐
‐ Irrelevant Questions have been removed from the questionnaire‐ Issues of misinterpretation of questions (Solved).‐ There is High transport cost.‐ There is 100% access to means of transport
This research will use background information and interviews,questionnaires will be administered to respondents selectedrandomly.
The data will be analyzed using statistical tools .
This research will use background information and interviews,questionnaires will be administered to respondents selectedrandomly.
The data will be analyzed using statistical tools .
Identify key findings/Analyze
Road Investment Affects
Marketactivities
Piloting
By: Omony Nobert email: [email protected]: Tony Plumbe2nd Reader: Jeff Turner
Figure 1: Map of Uganda
Figure 2: Map of the corridor
Does rail franchise competition damage potential for environmental performance?
Nicholas Forgham MSc Transport Planning Supervisor: Dr Caroline Mullen
• To investigate the justification for enhancing environmental
performance in rail franchises.
• To assess the effectiveness of the methods and measures
used by franchisees to improve their environmental
performance.
• To identify and discuss what barriers are preventing further
environmental performance improvements.
Context and Rationale
Objectives
Methodology
Key References
Dissertation Key Texts
Denscombe, M. (2011) The Good Research Guide. 5th edition. Maidenhead:
McGraw-Hill.
Department for Transport (2007) Delivering a Sustainable Railway. London: The
Stationary Office
Glover, J. (2013) Principles of Railway Operation. Hersham: Ian Allan Publishing.
Network Rail (2009) Network RUS: Electrification. London: Network Rail.
Network Rail (2013) Industry Strategic Business Plan - England and Wales:
Industry’s response to the High Level Output Specification for CP5. London:
Network Rail.
Rail Safety and Standards Board (2011) The Rail Industry Sustainable
Development Review. London: RSSB.
Rail Steering Group (2014) Long Term Passenger Rolling Stock Strategy for the
Rail Industry. London: Angel Trains.
The dissertation will adopt a qualitative structure using both
primary and secondary forms of data taking the form of:
• Documentary analysis of current reports on environmental
performance and the structure of the rail industry.
Denscombe (2014) suggests the wealth of information and
permanence of this research method can strengthen
investigations.
• Interviews with key stakeholders such as TOCs, Local
Authorities and Transport Campaign Groups.
• Analysis and evaluation of results to deliver conclusions on
environmental performance within the UK rail industry to
inform future policy direction.
Scope
The size and scale of the UK rail industry make it important for
this dissertation to clearly outline it’s intended scope as follows:
• Carbon Dioxide (CO2) reductions and how this is
achievable in the current railway industry from the
perspective of two geographically and operationally
different TOCs.
• To examine if environmental performance improvements
are motivated by economic or social reasons.
• To understand where the momentum for environmental
performance is in the current industry structure – TOCs,
ROSCOs, Network Rail.
Source: DfT (2012)
Source: RSSB (2011)
High Level Output Strategy Electrification by 2019
Source: Mark (2015) Source: Hampton (2015)
Source: Community Rail Lancashire (2015)
Dissertation Images
Community Rail Lancashire (2015) Accrington Station [online]. Available from:
http://www.communityraillancashire.co.uk/lines/east-lancashire. [Accessed 26th
April 2015].
DfT (2012) Rail HLOS electrification by 2019 [online]. Available from:
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/36
47/map-hlos-electrification.pdf. [Accessed 25th April 2015].
Hampton (2015) Together in electric dreams [online]. Available from:
http://www.roberthampton.me.uk/wordpress/wp-content/uploads/2015/03/bigger-
better-electric.jpeg. [Accessed 27th April 2015].
Mark (2015) 185113 at Eccles [online]. Available from:
http://mark5812.smugmug.com/keyword/Eccles/i-fxfCPvP/A. [Accessed 2015]
RSSB (2011) Sustainable Rail Program - Meeting Rail’s Carbon Ambition:
Carbon and cost reduction in the Industry Strategic Business Plan. London:
RSSB.
The demand for rail travel is increasing with over 1.4bn
passengers using the UK rail network in 2012, twice as many
as 1995 (Network Rail, 2013). This growth in demand is being
accommodated in Network Rail’s latest Control Period 5 (2014-
2019) which for the first time in recent years includes ambitious
plans for railway electrification.
The privately owned Train Operating Companies (TOCs), who
run services on Network Rail’s infrastructure, operate under a
franchise system specified by the Department for Transport
(DfT) which details performance criteria they must deliver
during their tenure.
However, the relatively short length of railway franchises,
compared to long term environmental performance
improvement projects, such as electrification, mean that
incumbent franchisees may be in the position of having to
endure service interruption and reduced revenues for
environmental performance gains which may not arise until the
next franchise (Glover, 2011).
Greening Leeds University to reduce CO2from its own business travel
• UK carbon target supposes the reduction of emissions(80% by 2050 and 34% by 2020). (1)
• Business travel is a key opportunity to curb CO2.
• The efficacy of some policies to encourage green
behaviour seems to be weak. Hence, it is necessary tostudy individual ‘s willingness to perform greeningbehaviour to achieve organisational goals. (2,3)
• Universities have a big role to play in tackling climatechange. The University of Leeds has agreed to meet the
government target.
• This goal can be contradictory with other UoL goals: more
academic travel is promoted with the idea of exchanging
knowledge and networking, often sustainable modes arenot available or increase time and cost.(4,5)
1. Background
Travel by Academic Staff and Departmental Managers
Short-term travel (i.e. conferences, lectures, projects)
Case study for Faculty of Environment (ITS,SEE,
Geography)
Concentrate on most promising incentives such as:
Figure 2 & 3. Video conference rooms in Roger Steven Building (Own picture).Figure 4 & 5 :Wikipediaand U.S Air Force. Figure 5: Train Station. (Own picture)
The aim is to understand UoL members individual intentions to support changes towards greening organisations, and how the Uol influences individual behaviour in business travel. Figure 1:Theory of Planned Behaviour (Ajzen,1985)
The objectives are:
2. Aims and Objective 4. Methodology
3. Scope
Train37%
Car (single ocuppant)26%
Car (with others)9%
Air7%
Bus or coach6%
Taxi7%
Walk6%
Others2%
Chart 1 Number of business trips in the “last month” based on Travel Survey 2013 (University of Leeds)
0%
5%
10%
15%
20%
25%
30%
35%
40%
Skype from desk Rewards Improve facilities Training Encourageteleconferences
IncreaseAwareness
Coverage percentage
Nodes
Chart 3. Perceptions.Policies that University should implement
to replace face to face meetings(based on Travel Survey 2013)
Modal ShiftCarbon Offset Teleconference
• Travel Survey 2013 (Leeds University)
• Report Scope 3 carbon emissions (Leeds University)
- Potential incentives to reduce CO2
- How to introduce incentives without contradict other UoL goals (reputation and recognition)
- Information to elaborate questionnaires
- Attitudes toward potential incentives
- UoL influence on academics behaviour(i.e. if Uol promotes exchange of knowledge and international collaborations; how would affect their careers if that participation is constrained)
- Perceptions about business travel (travel survey 2013)
- Current situation of business travel (amount of academic travel-Report scope 3)
Mixed Method approach
M. Lucila Spotorno - Supervisor: Astrid Gühnemann
n/a2%
Neutral, 34%
Disagree, 29%
Agree, 35%
Chart 2. Perceptions. People who fly should pay
the damage that air transport causes. (based on Travel Survey 2013, University of Leeds)
Explore the usefulness of Theory Planned Behaviour
Explore potential incentives to reduce CO2
Explore attitudes, subjective norms and (PBC)
Explore organisational influence in individual behaviour
Expected outcomes
Secondary data
1 2
Semi-structured Interviews
Academics and Managers
(6 interviews)
Academics (approx.390 from
ITS,SEE and Geography)
Purposive sample
Online Questionnaires
3
1. Climate Act Change 2008.2. STORME, T., BEAVERSTOCK, J. V., DERRUDDER, B., FAULCONBRIDGE, J. R. & WITLOX, F. 2013. How to cope with mobility expectations in academia: Individual travel strategies of tenured academics at Ghent University, Flanders. Research in Transportation Business & Management, 9, 12-20.3. STRENGERS, Y. Fly or die: air travel and the internationalisation of academic careers4. STRINGER, L. 2010. The green workplace: Sustainable strategies that benefit employees, the environment, and the bottom line, Macmillan.5. AJZEN, I. 1991. The theory of planned behaviour. Organisational behaviour and human decision processes,50, 179-211.
5. References
Perceived behavioural
control (PBC)
Subjective norms
Attitudes
Intentions Behaviour
Level of time consumed (Low (1),Medium (2),High(3)
Regional benchmarking of the British rail infrastructure
manager | A long panel approach
María Eugenia Rivas Amiassorho - MA Transport Economics | Supervisor: Dr Phill Wheat | 2015
𝑪𝒊 = 𝒇(𝒚𝒊, 𝒘𝒊; 𝜷) + 𝒗𝒊 + 𝒖𝒊
Deterministic frontier
Noise Inefficiency
Stochastic frontier
4. 1. Data Base
4. 2. Internal Benchmarking
4. 3. International Context
The internal or regional benchmarking will be conducted using a panel data set.
The maintenance and renewal costs (𝐶𝑖) can be explained through different
explanatory variables such as network size, traffic density and type, among
others (Nash and Smith, 2014) and can be expressed as follows:
where:
𝑤𝑖 = 𝑖𝑛𝑝𝑢𝑡 𝑝𝑟𝑖𝑐𝑒𝑠 𝑣𝑒𝑐𝑡𝑜𝑟
𝑦𝑖 = 𝑜𝑢𝑡𝑝𝑢𝑡 𝑣𝑒𝑐𝑡𝑜𝑟
𝛽 = 𝑝𝑎𝑟𝑒𝑚𝑒𝑡𝑒𝑟 𝑣𝑒𝑐𝑡𝑜𝑟
The results of the internal benchmarking will be compared with the international
benchmarking results with the purpose of contributing from an internal
perspective in the efficiency analysis of Network Rail.
It will be considered a deterministic frontier approach and a stochastic frontier
approach. The methodologies allow to build a “efficiency frontier”; zones located
on the frontier are efficient and the inefficiency of other zones is measured
through the distance from the frontier (Smith et al., 2008):
Kennedy, J. and Smith, A.S. 2004. Assessing the efficient cost of sustaining Britain's rail network: Perspectives
based on zonal comparisons. Journal of Transport Economics and Policy. pp.157-190.
Kumbhakar, S.C. and Lovell, C.K. 2003. Stochastic frontier analysis. Cambridge University Press.
Lema, D. 2010. Topicos de econometría aplicada. Eficiencia productiva y cambio tecnológico. Modelos de
fronteras estocásticas. UCEMA.
Nash, C. and Smith, A. 2014. Rail efficiency: cost research and its implications for policy.
Smith, A. 2015. The value, challenges and future of performance benchmarking in transport and infrastructure
regulation. ITS Research Seminar. Institute for Transport Studies, University of Leeds.
Smith, A. et al. 2008. International Benchmarking of Network Rail’s Maintenance and Renewal Costs. Report
written as part of PR2008.
Figure-3: Stochastic and deterministic
frontier, (Smith, 2015)
Figure-4: Stochastic vs Deterministic
frontier, (Lema, 2010)
This dissertation constitutes an extension of the internal benchmarking carried
out by Kennedy and Smith (2004) covering the period 1995/96-2001/02.
Stochastic inefficiency
Noise effect
Deterministic frontier
Observed cost
Deterministic inefficiency
Cost
Output
London North Western
London North Eastern
Western
Anglia
Scotland
Wessex
Sussex
Kent
Scotland
London North Eastern
London North Western
Anglia
Midland and Continental
Sussex
Western
Kent
Wessex
Scotland
London North Eastern
London North Western
Anglia
East Midlands
Sussex
Western
Kent & Continental
Wessex
Wales
Scotland
London North Eastern
North West
East Anglia
Midlands
Southern
Great Western
Figure-2: Configuration of zones
1995/1996 to 2003/20041 2004/2005 to 2007/20082 2008/2009 to 2010/20112 2011/2012 to 2012/20132
1Source: Kennedy and Smith (2004) and Annual Return to the Rail Regulator 2Source: Annual Return – Network Rail
0
200
400
600
800
1000
1200
1400
95/96 96/97 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 06/07 07/08 08/09 09/10
Hatfield Accident (October 2000)
Figure-1: Maintenance and track renewal costs
00/0
1 £
m
Maintenance
Track renewal
𝑪𝒊 = 𝒇(𝒚𝒊, 𝒘𝒊; 𝜷)
This dissertation aims to analyse the performance of the rail infrastructure
manager in Britain in the period 1995/96-2012/13 by fulfilling the next objectives:
1. Analysis of the regional performance (efficiency) over time with special focus
on its evolution after Hatfield accident.
2. Comparison of internal benchmarking results with international benchmarking
evidence in order to place the results in context.
2 | Motivation
1 | What is benchmarking?
3 | Aim and objectives
4 | Methodology
5 | References
External cost
benchmarking
Comparison of British infrastructure manager’s cost with European rail infrastructure managers
LICB (Lasting infrastructure cost
benchmarking) data set
Internal cost benchmarking
Comparison of British infrastructure manager’s
cost among different zones
. Kennedy and Smith (2004)
. Current dissertation
Data base
updating
Data base provided by Dr Phillip Wheat will be updated with information available on the website of Network Rail.
Potential risk: publicly available
information.
A zonal remapping will be required mainly as a consequence of the large period
under analysis (1995-2013) which implies differences in the configuration of
zones by the infrastructure manager (firstly Railtrack and secondly Network Rail).
The approach to be considered is to add zones rather than divide zones in order
to keep the consistency of the information: Deterministic
frontier
Estimation of Corrected Ordinary Least Squares (𝐶𝑂𝐿𝑆) which correct the Ordinary Least Squares
(𝑂𝐿𝑆) regression generating a cost frontier which is on or under the data (Kumbhakar and Lovell, 2003).
Stochastic frontier
Decomposes the unexplained variation in an inefficiency term and a random error term. Different
specifications will be considered.
The period covered by the dissertation (1995/96-2012/13) contributes to answer:
How the performance of the infrastructure manager has evolved after Hatfield
accident? What are the factors that contribute to explain it? What is the best
performing region? What are the potential cost reductions per region?
Benchmarking refers to comparative measures of performance. It is necessary to
keep costs under control because Network Rail is a national network monopoly.
The Office of Rail and Road (ORR) is the independent regulator which makes
sure that the rail industry in Britain is competitive and fair.
1994-2001/02 from 2002/03
LIMDEP and Stata 12 are the preferred software to conduct the cost analysis.
Why commute by car? – Modelling mode choice at University of Leeds.Student: Maria Poulopoulou Supervisor: Charisma Choudhury 2nd Reader: Stephane Hess
CHALLENGES UPDATED QUESTIONS
In order to identify more soft factors thatmight affect mode choice.
Likert Scale Questions
•Environmental awareness•Level of convenience and flexibility•Effect of weather conditions
In order to capture the social influence thatmight affect car sharing as an option.
Car Share Questions
•Knowledge and influence of people who car share•Reliance of people in family or not to be commuted•Split of the cost
In order to identify the available modesthat each household ones and that the staffmember is able to use.
Availability of transport
modes
Data•Missing Data
•Inconsistency across years
Modelling
•Cost Attribute: Specification of MPG for each engine size group.Specification of Average Price for each Fuel Type
•Missing Variables: Income, HH size
PRELIMINARY MODEL STRUCTURE
MOTIVATION METHODOLOGYParking Demand is a major problem in campusplanning and therefore the behavior of staffmembers should be understood (Bridgelall, 2014).Construction projects in Universities often decreasethe spaces available and worsen the existingproblem.
Total Spaces in all zones 1321Net off 262Freely available spaces on campus for staff 1059Spaces at Central Villlage 10
Spaces at Motaguw Button 31Total campus and Residence parking available to staff 1100
Current Parking Permit Data
DATA DESCRIPTION Source: Estate Office Time Period: 2008 and 2010 to 2014. Supplementary Data: Data for 2015 expected.1
•Literature Review•Specification of Data Requirements
2
•Data Collection•Design of Supplementary Questionnaire•Statistical Analysis
3
•Development of an econometric model•Specification of factors that affect choice of
car and mode choice in general•Evaluation of the results and their impact
in a parking policy
Car Parking LossesPa
rkin
g Pl
aces
Time Period
SCOPE OF THE STUDYTo investigate factors which are associated withthe choice of car instead of other travel modes andthat influence the mode choice behavior of the staffof the University.
Response Rate % Females % Males 2008 2304 59.4 40.6
2010 2162 58.5 41.5
2011 2665 60.2 39.8
2012 2564 59.4 40.6
2013 2559 58.5 41.5
2014 2567 60.4 39.6
Percentage of males and females for each year
Appraisal of Factors Influencing Mode Share Differences in West-Yorkshire
Manuel Martinez (MA Transport Economics) Supervisor: Dr. Judith Wang
Background & Study aims Since deregulation in October 1986, West-Yorkshire has experienced a substantial reduction of public
transport ridership over the last few decades whereas car modal share has been quite stable over the same period of time.
Especially noticeable is the case of bus patronage which modal share has fallen from 45% to 13% whereas rail share has risen lately from 1.5% in 2001 to 3.2% in 2011
(Leeds City Council, 2011) (Leeds City Council, 2011)
This study aims to identify the principal factors influencing both private and public transport patronage across the different areas of West-Yorkshire
Spatial Analysis
Methodology Literature review. Analyse the nature, data employed and econometric analysis of previous studies.
Decide from those, which variables and modelling approach can best fit in our case study
Data acquisition. Data collection & compilation of those variables considered potentially significant.
Spatial Analysis. Observe graphically potential relationships and principal factors driving differences in travelddddddddddddddddd behaviours for each mode
Econometric Analysis.
What’s next?
Model estimation Confirm expected influences
Find out potential reassonsotherwise
(+) Factor affecting patronage positively
(-) Factor affecting patronage negatively
High influence of rail accesibility on train trips generation
Large concentration of rail trips to Leeds CBD destination
Identify Rail-Road competition
Large proportion of bus trips originated within highly density areas.
Car use increases with distance to CBD
Low car ownership levels within CBD reveal Public Transport dependence
High influence of cycling routes
Leeds
Bradford
Carderdale
Kirklees
Wakefield
LeedsBradford
Kirklees
Explanatory variablesEXPECTED INFLUENCE
BUS RAIL CAR CYCLE
1 Distance to the nearest CBD + + - - - + + +2 Distance to Leeds centre + + +3 Population density + + - - - -4 Total commuters + + + +5 Bus Service + + + - - - - -6 Car ownership - - - + +7 Train station accesibility - + + + - -8 Income - + + +9 Cycling routes - + + +10 Student share + + +11 Parking bike facilities +12 Average slope +
Car ownership affected by rail accesibility
Leeds
Leeds
Effectiveness Evaluation of the Discounted Residential MetroCard Plan in West YorkshireMengjiao Long
Supervisor: Jeremy Toner; Second Reader: Jeremy ShiresUNIVERSITY OF LEEDS Institute for Transport Studies, University of Leeds, Leeds, UK
Introduction
Proposed Methodology
Background
Predicted Results
References
The Aim and Objectives
Visit to the Study Area
Related Literature
Review
Survey
Design
Indicator
Identification
Questionnaire Delivery
to the Control and
Experiment Group
GIS and
Census data
Comments on
the Plan
Data
Collection
Data Analysis
Result Report and
Conclusions
The Coverage and Scope
Data Category
ITS
In order to encourage the new house occupier to utilise public transport from the very start, the Residential MetroCard (RMC) Scheme first launched in 2006 is a joint initiative between Metro, WestYorkshire bus and rail operators. If a RMC agreement is in place, the new house occupier can enjoy:• One RMC for each household.• Totally free buses and trains in West Yorkshire for the first year, 25% discount in the second year and 10% discount in the third year.• Property developers pay the balance for each household.
A major problem facing West Yorkshire today is the increasingcar use and decreasing public transport use, especially the bus.Based on 2010 census data, in West Yorkshire:• 32% of households have no car, 43% have one car and 20%
two or more cars.• The bus patronage has been decreasing, a 5.5% decrease in
2010.• Mode share: 56.1% car, 22.2% bus, train 16%, 4.2% walk,
1.6% cycle.As a short term incentive (just 3 years), the RMC scheme isexpected to influence the travel habit of new house owners inthe long term, attracting them out of cars and taking publictransport as a preference.
A survey will be conducted among the targeted population.• Focus on all journey types.• Focus on households not individuals.• The target experiment population is the new house occupiers
with a provision of RMC scheme in West Yorkshire.• The target control population will be the new house occupiers
without a RMC scheme provision.
A point to point comparison will be applied to analyse thecollected data, mainly involving data:• Household basic information• RMC use• Car use• Public transport accessibility and quality
The aim of the topic is to:• Study the impacts of the RMC plan on travel behaviour
change in the target households.The objectives:• Identity factors that affect residents mode choice.• To identify whether the scheme has helped the property
developers mitigate traffic generation from new homebuyers in the short and long term perspective.
The predicted outcomes should be:
• Residents in the experiment group should have a higher
use of public transport, especially in the first year, and may
decrease in the following 2 years.
• RMC should restrain the car increase at least in the first 3
years.
• Residents’ awareness in the experiment area on public
transport use will be improved in the long term.
• Off-peak travels may have a higher use of public transport.
• Good degree of satisfaction from new residents.
• Thøgersen, J. and Møller, B. 2008. Breaking car use habits: The effectiveness of a free one-month travelcard. Transportation. 35(3), pp.329-345.
• Bonsall P. Do we know whether personal travel planning really works?[J]. Transport Policy, 2009, 16(6): 306-314.
• Chatterjee K. A comparative evaluation of large-scale personal travel planning projects in England[J]. Transport Policy, 2009, 16(6): 293-305.
• Möser G, Bamberg S. The effectiveness of soft transport policy measures: A critical assessment and meta-analysis of empirical evidence[J]. Journal of Environmental Psychology, 2008, 28(1): 10-26
Monica Kousoulou (200847158) Institute for Transport Studies (ITS) Supervisor: Dr Richard Connors
MSc (Eng) Transport Planning and Engineering UNIVERSITY OF LEEDS Second Reader: Dr Paul Timms
Objectives
Identify and incorporate the impacts of adverse weather in an
aggregate city transport model.
Quantify the impact of adverse weather conditions on urban
travel mode-choice and travel times.
Estimate the consequent impact on air quality(CO emissions)
and health (level of exercise and pollution uptake).
Identify mechanisms for the reduction of these weather
impacts in order to promote sustainable urban travel choices.
Background Weather causes a variety of impacts on the transportation
system. Day-to-day weather events such as rain, fog, snow, and
wind can have a serious impact on the mobility of the
transportation system users.
Capacity and speeds are two traffic parameters of a
transportation system that may be greatly affected by the
weather, resulting in change of travel times (Koetse and Rietveld,
2007).
Additionally, weather has a considerable impact on a series of
human decisions such as transport modal choice, trip
distribution, trip cancellation or postponement; altering roadway
users’ valuation of actual transport costs and travel times.
Methodology
Parameterisation of
weather scenarios
Adjustments to the
LMC model
Matlab coding and
Run of the simulations
Comparison of the results with
the base scenario
Literature ReviewLight Rain
Heavy RainLight Snow
Heavy Snow
Strong Wind
Impacts on
travel time
Impacts on
mode choice
CO emissions
estimation
Health impact
assessment
Model DescriptionAn integrated land use, transport planning, air quality and health impact assessment model
for a linear monocentric city (Wang and Connors, 2015).
1. Characteristics of this linear monocentric city
An urban corridor leads to a central business area(CBD).
Population is distributed continuously along this corridor and commuters have the same
destination, the CBD.
Available modes : walk, bicycle, train and car .
Access to the road at any location and to the nearest rail station by walking or cycling.
Linear City CBD
CBD
E 12
Length of the City = L
2. E
quili
briu
m A
naly
sis Commuters Objectives
Travel Time Travel Time Reliability Monetary Cost
Three-Objective User Equilibrium model
(Travel Time Budget Surplus (TTBS))
Vehicle Emission Prediction
Travel Time Modal Split Individual
Exercise Level
Pollutant Uptake Estimation
Total CO
emissions
Individual
Pollutant Uptake
3. A
ir Q
ualit
y &
Hea
lth Im
pact
Ass
essm
ent
Preliminary ResultsBase Scenario: Normal Weather Conditions
References Available at: http://transportdissertation.simplesite.com/
Hot Weather
Normal Weather
(Wang and Connors , 2015)
Hypothesis 1: As house prices increase, the house price upliftper minute of time saving from public transport decreases.
Background and Proposal• High congestion on the A660 corridor• Tram proposal scrapped in 2005 due to escalating costs• Trolleybus proposed as cheaper alternative at £250 million to run
between Holt Park in the north-west to Stourton in the south-east• Electrically powered by overhead cables• 65% route segregation, Peak frequency of 10 services per hour • Due to open in 2020 if approved by government
Current Literature• Travel time is main transport characteristic reflected on house prices• Current hedonic pricing methods only give overall percentage change
in house prices• Steer Davies Greave (2013) used a linear model from Volaterra (2008)
to predict house price changes from the Leeds trolleybus, though the model is only a good fit to actual house prices up to about £150,000, after which the model overestimates house prices
• Du and Mulley (2012) found areas were affected differently in the Tyne and Wear region from changes in public transport accessibility, by use of geographically weighted regression. Larger percentage changes in house prices per change in accessibility occurred in poorer areas compared to richer areas
Value this work will add to the subject area• Provide clear evidence of house price uplift deviating from a
uniform uplift when certain characteristics are strong• Provide solid grounding for further research into different
house price uplift from transport investment
Hypothesis 2: As car ownership increases, the house price uplift per minute of time saving from public transport decreases.
Map of Local parameter estimates of house prices in Tyne and Wear, associated with Public Transport Accessibility
Methodology• Using past investments in transport infrastructure to
assess the property price changes caused by changes in travel time
• Use Arc GIS Geographically Weighted Regression to identify house price changes per travel time saving
• Use of colour coded maps to compare areas differing in car ownership and previous property prices
• Further regression analysis used to identify the extent car ownership and previous property prices are responsible for changes in house price uplift per travel time saving
• Use of actual house prices from the UK Land Registry • Past UK tram investments used including Manchester
Metrolink, Nottingham tram and Edinburgh tram • Non UK trolleybuses not considered due to ridership
differences between Europe and the rest of the world, (Currie and Delbosc, 2013), modal split differences between the UK and Europe, except Germany (European commission, 2012, p.47), different paced housing markets in the UK and Germany (Hilbers et al, 2008)
Modelled House Prices Against Actual House Prices
References• Carey-Campbell, C. 2013. A Presentation to Leeds City Council on Wednesday 8th May Regarding the Proposal NGT Trolleybus Scheme. North Leeds life. [Online]. 9 May. [Accessed
22 April 2015]. Available from: http://www.northleedslifegroup.com/• Currie, G. and Delbosc, A. 2013. Exploring Comparative Ridership Drivers of Bus Rapid Transit and Light Rail Transit Routes. Journal of Public Transportation [Online]. 16 (2), pp.47–
65. Available from: www.researchgate.net• Du, H. and Mulley, C. 2012. Understanding spatial variations in the impact of accessibility on land value using geographically weighted regression. Journal of Transport and Land
Use [Online]. 5 (2), pp.46-59. Available from: https://www.jtlu.org/• European Commission. 2012. EU Transport in Figures: Statistical Pocketbook 2012. [Online]. Luxembourg City, Luxembourg: European Union. [Accessed 16 April 2015]. Available
from: http://ec.europa.eu/• Hilbers, P. Hoffmaister, A. Banerji, A. and Shi, H. 2008. House Price Developments in Europe: A Comparison. [Online]. Washington D.C., USA: International Monetary Fund.
[Accessed 16 April 2015]. Available from: https://www.imf.org/• New Generation Transport (NGT). No Date. New Generation Transport’s Website. [Online]. [Accessed 14 April 2015]. Available from: http://www.ngtmetro.com/• Office of National Statistics (ONS). 2011a. 2001 vs 2011 Census – Car Ownership. [Online]. [Accessed 14 April 2015]. Available from: http://www.ons.gov.uk/• Steer Davies Gleave. 2013. New Generation Transport for Leeds: Improving Connectivity, Adding Value. [Online]. Leeds, United Kingdom: New Generation Transport (NGT).
[Accessed 15 April 2015]. Available from: www.ngtmetro.com/
(NGT, No Date)
(Carey-Campbell, 2013)
9. POTENTIAL IMPLICATIONSBringing residents closer to destinations and providing basic access to services and viable alternatives to driving might encourage less driving, however affordability needs to be considered
1. INTRODUCTIONCities in developing countries are experiencing massive and rapid urbanisation• In Kenya 60% of the urban population live in the capital city, Nairobi (JICA
2013)• City characterised by extreme congestion, poor public transport and car
dependency• Current advocacy for compact, high density mixed use development with
good transit service to accommodate growth and influence travel behaviour
2. OBJECTIVES• Is the built environment capable of influencing peoples travel patterns in
unregulated environments or do peoples travel preferences dictate their neighbourhood choice?
• Inform policy development
3. HISTORY AND URBAN FORM• Urban planning follows colonial segregationist policies• Nairobi East was restricted to African residents, while the Western regions, for European settlers
• The current data on settlement patterns, distribution of social services and facilities suggests that inequalities between West and East may be reflective of the disproportionality of resources caused during this earlier period
6. METHODOLOGY4. LITERATURE REVIEWTravel behavior is complex
THE URBAN FORM AND ITS INFLUENCE ON TRAVEL BEHAVIOUR: A CASE STUDY OF NAIROBIMaina Gachoya Msc Transport Planning and EngineeringAnn Jopson (Supervisor)
TRAVEL PATTERN
BUILT ENVIRONMENT
ATTITUDES
BELIEFS
SOCIO
ECONOMICS
Results
Oral Presentation Written dissertation
Analysis
Data Cleansing Multivariate Analysis
Data Collection
Questionnaire InterviewsTransport surveys and spatial studies
Literature Review
• Multivariate analysis commonly used to test the relationship between these three key areas and determine their influence on travel patterns
• Stead (2001) found that socio‐economic factors explained more than 50% of the variation in the amount of travel however did not account for attitudes
• Kitamura et.al. (1997) attempted to capture behavioural aspects through a travel diary and found attitudinal variables could explain the highest proportion of variation in the data
• Handy et.al. (2005) captured attitudes on both urban form and travel characteristics determined that differences in travel behaviour between suburban and traditional neighbourhoods are largely explained by these and a causal relationship exists
Research Gaps:• Most studies not transferable: fail to consider how unstructured
urban form influences travel behaviour in their transport studies (Vasconcellos, 1997)
• Studies are UK/US based which are different in terms of political, cultural and historical contexts4. Kibera 780 person/acre2. Kilimani 12
person/acre
5. Buruburu 150 person/acre
General Change in Typology
1. Karen 2 persons/acre
3. Eastleigh 200 person/acre
7. DATA COLLECTIONA questionnaire was piloted to capture four key criteria : 1. Travel attitudes : Format based on theory of
planned behaviour principles 2. Preferred urban form and perceptions: Adopted
from studies by Handy et al(2005)3. Travel Pattern: travel time and distance4. Socio‐ economic characteristics
5. RESEARCH QUESTIONSa. Is there a relationship between the built environment, attitudes and socioeconomics?b. To what extent do these factors individually or in combination influence travel patterns?
8. PRELIMINARY RESULTSa. 12 responses received from a pilot of 20
questionnaires.b. Survey conducted during a period of traffic
management implementation might have biasc. Car use predominant mainly due to convenience,
time efficiency and affordabilityd. Rent, availability of water and proximity to work
ranked highly in influencing residential conditionse. Some responses indicate preference to living far
from the “chaos” of CBD
NEW TECHNOLOGY AND RESILIENCE IN TRANSPORT SYSTEM
2. Objectives•Understanding the road infrastructure based Intelligent Transport Systemparticularly pertaining to ATMS and ATIS.•Analyzing a road network in Greater Manchester from data provided byTFGM and determining the transport resilience using Passive BluetoothSensors with respect to travel times from accidents and their impacts onthe remaining road links.
•Analyzing the scope of this technology for future considerations.
1. Purpose of my work:• Bluetooth is the latest wireless technology currently in use with
characteristics of interference resilience and power efficiency.• The reason I chose to study the following road and network is
since, the A6 is one of England’s historic and longest A roadrunning past Manchester in the North South direction,experiencing high number of accidents, giving a strong analysisfor my research.
• Carrying out an in depth analysis of this system to improve thescope for future consideration.
3. Research methodologyThe journey times of vehicles in the months of October and November2014 are analysed and related to the accidents occurred on the chosenroute. Resilience is determined using two measures; Mobility andRecovery.1. Mobility – The total time is observed, where the average speed of the
vehicle over the street is less than the prescribed speed limit. Theother measure is Volume/ Capacity ratio expressed in percentage witha V/C value greater than 100% indicating extreme congestion.
2. Recovery - Analysing the total time required to reduce congestion,calculated by analysing the speed of the vehicles exceeding therespective speed limit of the street and by observing the V/Creturning to its acceptable limit.
Road Network in ManchesterCase study area
Key References1. Grant Muller and Usher (2013) Intelligent Transport System: The propensity of environmental and economic benefits: Technology forecasting and social change. Vd –82, pp 149-166..2. Murray- Tuite, P. M. (2006, December). A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions. In Simulation Conference, 2006. WSC 06. Proceedings of the Winter (pp. 1398-1405). IEEE.
MAC IDs
http://www.libelium.com/vehicle_traffic_monitoring_bluetooth_sensors_over_zigbee/
4. Expected outcome• Bluetooth devices being extremely
sensitive with journey times tounexpected situations.
• Clear difference spotted by thedevices with changes in journeytimes on the remaining links due toaccidents.
• Accurate resilience determinationusing the devices giving empiricalresults.
Data from Transport for Greater Manchester showing sensitivity of device
Match count
The above graph shows a sudden peak in the journey timesobserved on the A6 on the 17th November 2014 with a widegap and no vehicle data recorded clearly illustrating thesensitivity of the devices.
Transport For Greater Manchester Database
Sensors placed in Manchester
Levels of Autonomous Vehicles• Level 0 (no automation)• Level 1 (function‐specific automation)
e.g. cruise control, assisted braking• Level 2 (combined function automation)
e.g. cruise control with lane assist• Level 3 (limited self‐driving automation)
– Vehicle automated, but monitors for situations where driver is necessary
• Level 4 (full self‐driving automation) –Vehicle fully automated
How will Autonomous Vehicles (AVs) alter and inform the appraisal and popularity of public transport in the UK?
1. Introduction and background
3. Methodology 4. Expected conclusions and implications
2. Key research questions
Key referencesAnderson, J.M. et al. 2014. Autonomous Vehicle Technology: A Guide for Policymakers. Santa Monica: Rand Corporation.Begg, D. 2014. A 2050 Vision for London: What are the Implications of Driverless Transport? Reading: The Javelin Partnership.Fagnant, D.J. and Kockelman, K. 2014. Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations.Washington, DC: Eno Center for Transportation.Le Vine, S. and Polak, J. 2014. Automated Cars: A Smooth Ride Ahead? London: Independent Transport Commission.Litman, T. 2015. Autonomous Vehicle Implementation Predictions: Implications for Transport Planning. Victoria: Victoria Transport Policy Institute.
Laurence Venables – MSc Transport Planning Supervisor: Dr. Zia Wadud
• What stage is AV technology currently at?• How might AVs change the appraisal of public
transport projects in the UK?• Should specific policy measures be
introduced prior to the introduction of AVs on the UK’s roads?
• AV technology could significantly reduce public transport operator costs• Public transport operators may need to embrace AV technology to limit modal shift to
private AVs• Governments/LAs may have to subsidise AV investment for PT
• More productive journeys and removing the search for/inconvenience of parking may increase car demand and cause congestion
• Transport models may have to be recalibrated to represent increased capacity of AV highways or reflect changes in travel behaviour
• Further research to be done on possible uptake of AV vehicles• A lengthy implementation may create traffic management and demand forecast problems
• Will public transport operators need to embrace AV technology to maintain or increase their mode share?
• Will it be private or public transport to embrace AV technology first?
• How might AV technology change passengers’ Value Of Time?
• Will AVs cause more or less congestion?
Costs Costs
Demand Demand
Journey Times
Demand Model
PTModel HighwayModel
Car-available trips
PublicTransport Car
VOYAGER
EMME
Walk+Cycle
Fast Mode Choice(car vs. public transport)
Parking Choice
Time Period Choice
Trip Distribution
On Street
Trip Distribution
Public Transport Mode Choice(rail vs. bus)
Off Street
Park-and-Ride
Rail
Bus
Time Period Choice
NGT
Active Mode Choice(motorised vs. active)
Time Period Choice
Trip Distribution
Leeds Transport Model
• Scenarios could be modelled in Leeds Transport Model assuming AVs have been implemented:
• value of time change (productive journeys)• remove parking search/charge• increase vehicle occupancy (greater car sharing)• remove walk time (door‐to‐door journeys)• reduced PT fares (automated fleets, lower
running costs)• Outputs from modelled scenarios can be analysed
and compared to base year (without automation)• demand totals, vehicle kms
(Litman, 2015)
AV implementation projections
Major stakeholders• Google, Audi, Volvo,
BMW (and other manufacturers)
• Government, Local Authorities, PT operators
• Oxford University, Uber Taxis, UK Autodrive
What are AVs?Autonomous Vehicles. Capable of navigating public roads without human input. Can negotiate junctions, park and make emergency manoeuvres.
(Huffington Post, 2014)(Transport Systems Catapult, 2015)
(Begg, 2014)
(KPMG, 2013)
(WYCA and LCC, 2015)
(AECOM, 2011)
ANALYSING THE RELATION BETWEEN PUBLIC TRANSPORT AND SOCIAL EXCLUSION IN INNER-CITY AND SUBURBS OF BUENOS AIRES
LUCILA CAPELLI - [email protected] SUPERVISORS: JEFF TURNER & FRANCES HODGSON
1. JUSTIFICATION & BACKGROUND
-In the Metropolitan Area of Buenos Aires (MABA) there are almost
340,000 of households with unsatisfied needs (INDEC, 2010).
-There is a broad consensus around the idea that problems with transport
provision can reinforce social exclusion and that public transport plays a
key role in guarantee access to employment, rights and goods (Social
Exclusion Unit, 2003, Lucas, 2004, Hine and Mitchell, 2003, Church et al., 2000 & Farbiarz
Castro, 2013).
-There is a lack of data and analysis regarding public transport access in
deprived areas of Buenos Aires.
2. MAIN OBJECTIVE
Determine the existing disparity of public transport system in the MABA
and its relation with social exclusion.
3. RESEARCH QUESTIONS
4. METHODOLOGY -Mapping primary data sources (especially National Census of 2010) and
transport supply using GIS (unit of analysis: census radius)
-Calculation of indexes, following Farbiarz Castro (2013):
-Analysis of particular results in case study areas, including relation with
planning projects.
Weaknesses: it is not a forecast demand study. Some data is not publically
available. Lack of official data about travel behaviour and accessibility.
Strengths: it will give a cross-sectional account of the relation between
socio-economical profile of households, transport provision and impact on
BRT and planning projects.
5. CASE STUDY AND SPECIFIC AREAS OF ANALYSIS
-Currently, the MABA has almost 13,000,000 inhabitants. MABA includes
Buenos Aires City district and 24 municipalities of Buenos Aires Province as
it is shown in Figure I.
-While population in Buenos Aires City has not grown in the last 50 years,
in the suburbs from 1947 to 2010 the population has increased six times
(from 1,730,511 to 9,916,715 inhabitants).
Case study 1: La Matanza municipality is located in Buenos Aires Province
and it is the most populated of the suburbs of MABA. Also, it presents the
biggest intercensal population variation (41.8%). Figure II shows deprived
households, existing transport infrastructure and projected BRT.
Case Study 2: The “Villa 21-24” is a slum in the south of Buenos Aires City.
Although the population is not increasing in the city, it grew a 52.6% in
slums (48% in the Villa 21-24). It is close to the Business Central District of
MABA and important transport infrastructures (See Figure III).
6. INDICATIVE RESULTS -Preliminary analysis indicates much lees public transport provision in
areas with higher levels/proportions of deprived households.
-Urbanisation increasing very quickly but no evidence that public transport
provision is keeping pace.
-Most households are in the south of the MABA.
-There is a lack of transport provision in the suburbs, especially in affecting
case study areas. Poor interurban train service in most of the MABA
corridors.
-Lack of metropolitan view: MABA has not a unified transport authority.
Policy decisions are not made after a planning process. There is not a land
use´s MABA policy, and less and integration between urban development
and transport.
7.REFERENCES CHURCH, A., FROST, M. & SULLIVAN, K. 2000. Transport and social exclusion in London. Transport Policy, 7, 195-205.
GREAT BRITAIN. SOCIAL EXCLUSION UNIT 2003. Making the connections Final report on transport and social exclusion: summary.
HINE, J. & MITCHELL, F. 2003. Transport disadvantage and social exclusion. London, Aschgate.
LUCAS, K. 2004. Running on empty. Transport, social exclusion and environmental justice. Bristol.
FARBIARZ CASTRO, V. 2013. Measuring the disparity in Bogotá's public transport system. University of Leeds.
BOCAREJO S., J. O. H., D.R. 2012. Transport accessibility and social inequities: a tool for identification of mobility needs and evaluation of transport investments. Journal of Transport
Geography, 24, 142-154.
CARRUTHERS, R. D., M; SAURKAR, A. 2005. Affordability of Public Transport in Developing Countries. In: GROUP, T. W. B. (ed.) Transport Papers.
CURRIE, G. 2004. Gap analysis of public transport needs. Measuring spatial distribution of public transport needs and identifying gaps in the quality of public transport provision.
Transportation Research Record. The Journal of the Transportation Research Board, 1895, 137-146.
CURRIE, G. 2010. Quantifying spatial gaps in public transport supply based on social needs. Journal of Transport Geography, 18, 31-41.
DEPARTMEN OF TRANSPORT 2006. Accessibility Planning Guidance. In: DFT (ed.) Guidance
INDEC 2010. Censo Nacional de Hogares y Población 2010.
SECRETARÍA DE TRANSPORTE 2007. Investigación de Transporte Urbano Público de Buenos Aires (INTRUPUBA). In: NACIÓN, S. D. T. D. L. (ed.).
BUENOS AIRES CITY GOVERNMENT. 2015. Buenos Aires Data [Online]. Buenos Aires City Government. Available: http://data.buenosaires.gob.ar/dataset [Accessed 10/04/2015 2015].
IGN. 2015. Base de datos geografica [Online]. Instituto Geografico Nacional. Available: http://www.ign.gob.ar/sig [Accessed 10/04/2015 2015].
Figure I. Percentage of deprived households per census radio with interurban rail, metro and BRT infrastructure of MABA.
Source: map prepared by author in base of INDEC (2010); Buenos Aires City Government (2015) and IGM (2015)
Figure III. (a) Map of Case study 2 (“Villa 21-24”) with % of deprived households, transport infrastructure and planned projects. (b) Google Earth view of neighbourhood
Figure II. (a) Map of Case study 1 (“La Matanza” municipality)
Source: prepared by author in base of INDEC (2010); Buenos Aires City Government (2015) and IGM (2015)
Source: prepared by author in base of INDEC (2010); Buenos Aires City Government (2015) and IGM (2015)
b. a.
DOES THE DRIVER CONTROL THE CAR DURING INTERACTION WITH SECONDARY TASKS?Konstantina Solomou
MSc Transport Planning and EngineeringSupervisor: Dr.Natasha Merat Second Reader: Tyron Louw
Stage 1: Select the appropriate type of secondary tasks (interesting and boring) by using a questionnaire, which is going to be administered to 24 people.
Equipment: Driving performance is going to be evaluated by using the University of Leeds Driving Simulator
Stage 2: Main Experiment: 24 car drivers(20-59 years) are going to use driving simulator, who should meet the following requirements:
Valid driver’s licence >3 years driving experience
Normal or corrected to normal visual acuity
Different perspective comes from literature: Automation is perceived as safety enhancing,
whereas the distraction related risks of using mediaare increasingly acknowledged (Strayer & Johnston,2001).
A previous study using in-vehicle video footagefound that 22% of crashes were caused by driverdistraction. It also showed that the possibility tocrash is two or three times bigger while drivers usea secondary task at the wheel (National,HighwayTraffic Safety Administration, 2006).
Figure 1 shows the number of total drivers whowere involved on fatal accidents and the proportionof them who were distracted.
According to Verwey and Zaidel(1999), performing asecondary task under certain conditions, increasedtask engagement and alertness. Furthermore,Gershon et al.(2009) found that an interactivecognitive task helped improve driver performanceand mental state.
Background Methodology
The current study aims to test how the two types ofsecondary tasks (boring and exciting games on iPad)affect driver performance when driver meets unexpectedincident on the road and has to take control of the car.
Objective
Driving Performance Measures
.
Expected Findings
Based on a previous study, (Merat et al., 2014)the automation is expected to reduce workload.However, the change into manual mode whiledriver's attention is attracted by the secondarytasks, will affect negatively the driving safety.
The worst performance is expected to beobserved when drivers in the automated modeare going to regain control of driving whiledistracted by the exciting secondary task due tothat their attention will be attracted more.
Progress of the experiment
Boring/exciting: secondary tasks (games on IPad)Critical incident: A car in front brakes unexpectedly
References:1)Gerson, P., Ronen, A., Oron-Gilad, T., & Shinar, D. (2009). The affects of an interactive cognitive task (ICT) in suppressing fatigue symptoms in driving. Transportation Research Part F, 12, 21-28.2) Merat, N., Jamson, H., Lai, F., Daly, M., & Carsten, O. (2014). Transiton to manual: Driver behaviour when resuming control from highly automated vehicle. Human Factors, 27,274-282.3)National Highway Traffic Safety Administration. (2006). The Impact of Driver Inattention on Near Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data. DOT HS 810 594. 4) Jamson, H., Merat, N., Carsten, O., & Lai, F. (2013).Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Human Factors, 30 ,116-125.5)Strayer, D/l., & Johnston, W. A. (2001). Dual-task studies of simulated driving and conversing on cellular elephone. American Psychological Society, 12, 462-466.6)Verwey, W.B. & Zaidel, D.M.(1999). Preventing drowsiness accidents by an alertness maintenance device, Accident Analysis and Prevention, 31, 199-211.
Figure 1: Drivers involved in Fatal crashes by age,2011
Figure 4: Behaviour following “Beep” and driving performance measures
Figure 2: The University of Leeds Driving Simulator (Jamson et al., 2013)
Figure 3: Progress of the experiment
Source: ( National Highway Traffic Safety Administration, 2006).
COMMUTING AND TRANSPORT ATTITUDES IN LUANDA
Situation of Luanda’s Roads
1/3/5 Street of Luanda.2/4/6 Highly congested roads at peak hours.
Researched by Google.
Institute for Transport StudiesMSc (Eng) Transport Planning and Engineering
ANGOLA
ZAMBIA
NAMIBIA
DEM.REP. OF THE CONGO
SOUTHATLANTICOCEAN
LUANDATHE CAPITAL OF ANOGLA
34%Population of
LuandaPopulation of
Angola
STUDENT NAME l KILSON GOUVEIASUPERVISOR l TONY PLUMBESECOND READER l TONY WHITEING
Data Collection(Primary source-Questionnarie)
Data Collection(Secondary Source - Existing Source) Data Analyses Writing UP/Conclusion Text RevisionLiterature Review
Progress Map
Research objectivesTo identify travel patternsTo understand commuters’ attitudes towards shifting from private car use to public transport (or other modes)To indicate the extent to which changes in travelers’ habits could lead to a reduction in congestion levels
Research questionsHow does urban form affect travel patterns?How does accessibility influence commuters’ modal choice?Why does the private car appear to be the preferred mode for commuting?How does the use of non-motorized modes would help reducing congestion?How do commuters’ perceive costs?Would a reduction in car ownership levels encourage more people to use public transport? What would need to happen?
Methodology
0203
04
01
Qualitative Data Analyses Quantitative Data Analyses
CorrelationNon-parametric tests In-depth interview
Likert scale analyses
Regression analysespeople coming
to municipal marketCivil servants
Luanda - capital city of Angola - has 6.5 million* of people.Over 2 milion cars.Highly congested roads at peak hours.Urban sprawled development.Road accidents kill ~1000 people/year*Time spent commuting ~ 4 hours/dayWorkplaces largely established in city centreInefficient and unreliable public transport system
Background
Public Transport network integration connecting city centre to Via Express: Bus lane and BRT.
Bus Lane
BRT Vias
Terminals
LUANDA
(Number of people)
ANGOAUSTRAL
4,495,723
MACON SGO TCUL TURA
6,318,771
5,344,497
995,508156,322
Population projection in Luanda . 2014-2030
Population of Luanda
Concentration of the national population in Luanda
27% 27% 29% 31%34%
6.5 Millions 6.8 Millions8.4 Millions 10.6 Millions 12.9 Millions
22%
18%66%
17%
33%50%
28%
22%
50%
2014Time line 2015 2020 2025 2030
Percentage of trips undertaken by each mode for the Luanda Province in 2030
Public TransportPrivate carNon mototrised
Number of BUS companies operating in Luanda in 2014
STEP
STEP
STEP
STEP
COMMUTING
MODE CHOICES
URBAN FORM TRAVEL BEHAVIOUR
SOURCE : INTR.2014
SOURCE : PDGML.2014
SOURCE : INE, CENSUS PRELIMINARY RESULTS.2014 ; PGML.2014
SOURCE : PDGML.2014
SOURCE : INTR.2014
Background
Current researches about pedestrian crossing’s evaluation could be allocated into two groups:
•Comprehensive assessment before building a crossing including location, highwayfacilities, visibility, complexity, crossing traffic, vehicles and road accident(Note, 1995).
•Evaluation of existing crossings in safety perspective including pedestrians’ behaviours,accident data analysis, etc(Martin, 2006; Webster, 2006; Davies, 1999).
However, less attentions were paid on how well does the existing pedestrian crossing performin adjusting the different priorities (i.e. delay caused by pedestrian crossings) of pedestriansand vehicles which could be used for making decisions about the improvements of existingcrossings.
Under this circumstances, research will focus on the delay caused by pedestrian crossings andreasons behind individual situations to provide useful factors that could be considered whenevaluating existing pedestrian crossings
Priority Evaluation of Existing Pedestrian Crossings
Assessment
Framework
before building
Site condition
Location,
Crossing flow,
Facilities, etc.
SafetyPerviousaccident record
Difficulty of crossing
Waiting time, Area features
CostInstallation cost, operation cost.
Assessment
Framework
after building
Safety
Location,
Crossing flow,
Facilities, etc.
Accident record,Pedestrians’
behaviours
Difficulty of crossing
Less attentions
CostInstallation cost, operation cost.
Jiajun Zhuo Email: [email protected] Msc (Eng) Transport Planning and Engineering Supervisor: Dr. Frank Lai
Proposed Analysis
Effects from traffic flow, group of pedestrians,time period, pedestrians’ behaviour to delay ofpedestrians and vehicles(positive or negative,what’s the extent range of effects).
Expected Contribution
This research will put efforts to assess theeffectiveness of existing pedestrian crossings interms of users’ priorities, which could be used toreconsider whether the existing pedestriancrossing is still suitable or need to be improvedafter being allocated.
Main References
Note, L. T. 1/95, April 1995. The Assessment of Pedestrian Crossings.
Note, L. T. 2/95, April 1995. The Design of Pedestrian Crossings.Davies, D. G. (1999). Research, development and implementation of pedestrian safety facilities in the United Kingdom. Publication No. FHWA-RD-99-089. Federal Highway Administration.
Martin, A. (2006). Factors influencing pedestrian safety: a literature review(No. PPR241). TRL.
Webster, N. (2006). The effect of newly installed Puffin crossings on collisions. Transport for London Street Management.
Observation Table
Proposed Methodology
Scope:•Non-signal control (Zebra crossings)•Fixed time control (Pelican crossings)•Dynamic control (Puffin crossings)
These three groups are the most common crossing types in UK with different working principles of priorities control(Davies, 1999).
Key Data
Delay time of pedestrians and vehicles, sitetraffic flow, time period, pedestrian group(elderly and disable, young), pedestrianbehaviour, facilities feature(midblock or not),signal control, these above are based onassessments for planning pedestriancrossings (Martin, 2006).
Research Questions
Q1: What factors would affect delay time ofexisting pedestrian crossings?(Situationswhere delay would be changed)
Q2: How could these factors influence delaytime? (e.g. vehicle gap in different conditions,ability of crossing of pedestrians, differentproportion of vehicles and pedestrians, illegalcrossing or distracted from signals)
Q3: Despite of externalities, what kind ofcrossings would be affected more effectively ?(e.g. signal control type and mid-block )
Collecting Method
Site observation, video camera could be used assupport. Take one sample from a group ofpedestrians/vehicles, record their waiting time(from the mount target are stopped till they getthe access to pass, if there is a mid-block, twoparts of delay should be added up), pedestriancharacteristics, time period, behaviours, facilitiescondition(e.g. mid-island, signal type).
Analysis Method
As the observation table categorized, delay timecould be divided into several groups. T-testwould be used before analysis to calculate arepresentative average delay time for eachconditions.
Then, by using control variable method, delay time could be assessed with only one factors different while keeping others in the similar level.
Zebra Pelican Puffin
Type: Mid-block:
Date: Traffic flow:
Pedestrian features Behaviour
Vehicle△
elderly/disable
○
young Illegal Normal Distracted
Time period
Rush
hour
lunch
break
Off-
peak
The Effect of Flow Change on Travel Time in Headingley
Student: Joseph Matar INSTITUTE FOR TRANSPORT STUDIESUNIVERSITY OF LEEDS
Supervisor: Dr. James Tate 2nd Reader: Prof. Simon Shepherd
Trav
el T
ime
In S
ec
Flow in PCU/hr/lane
Travel Time – Flow Relation
References• Akcelik, R. 2003. Speed-flow models for uninterrupted traffic facilities• Lei, H. Predicting corridor-level travel time distributions based on stochastic flow and capacity variations• Charlesworth, J. 1975. Relation between travel-time and traffic for the links of road networks controlled by fixed-time signals.
Congestion is a non-linear phenomenon, once you goabove the capacity threshold, each car you add to theflow, it adds a non-linear value to the travel time. If theflow value varies from zero to a certain free flowvalue, there is no congestion and the travel time islow, and even constant in some cases. However, afterreaching the road capacity, congestion starts. A queueshapes up and shockwave is seen, as a result thetravel time increases severely. The relation betweentravel time and flow rate is not linear, the relation isrepresented in the graph below:
IntroductionHeadingley road is an urban, two lane road located inLeeds, West Yorkshire, England. This road is congested,especially in the a.m. peak hours, towards the city centre.
Methodology Aimsun is an integrated transport modelling software, it
visualizes the network and calculates travel time of vehicles. The data collected will be compared with Aimsun result.
Travel time – flow relation can be represented by the following equation: 𝑡 𝑓 = 𝑎 + 𝑏𝑓𝑛
With: o a is the free flow travel time in secondso f is a variable representing the flow in PCU/hr/laneo b is a constant
BackgroundIn Sweden, the flow was reduced by 20%, so 80%of vehicles are still using the road, however, as wecan see in the pictures below that there was nomore congestion. The picture on the leftrepresents the old situation, and the picture on theright represents the case with the reduction of20% the flow.
DataData will be provided from ITS, traffic data will be providedfrom the loop detectors to find the number and type ofvehicles crossing Headingley road, and also their traveltime. The data will be analysed, to see the change of flowthroughout the week and how it varies during the day.Headingley has a maximum speed of 30 mph, which isrelatively low. A small absolute difference in travel speeds atlow speeds has a greater effect on travel time than the sameabsolute difference at higher speeds, therefore a smallchange in speed will have a significant effect on the traveltime.
ObjectiveThe objective of the dissertation is to find thatturning point, when the congestion starts, and thetravel-time to flow curve grows rapidly.
The effect of disruption on travel behaviour following workplace reorganisation:City of York Council
Research Questions
Aim to assess how the disruption of changing workplace can catalyse travel behaviour change.
Objectives
1. Quantify post reorganisation travel behaviour changes, over four years.
2. Assess how staff have adjusted their working patterns.
3. Examine wider reaching, longer term, changes in lifestyle, work and travel.
Considerations
Longitudinal Study
Account for background changes over time: National increase in active travelExternal variables: Transport network changes
Dataset Limitations: Survey: self-selection bias Staff joining date: Before or during
reorganisation Staff turnover: need large dataset Relocation may skew dataset:
o e.g. city centre location may attract and retain employees who favour active and public transport.
Joanne Best
Background
West Offices
Up to 1,400 staff > 1,100 workstationsAt home working
Scope
West Offices & Hazel Court
Previous study:Year 1 in 2013Year 2 in 2014(Shires, J. 2014)
This study:Year 3 and 42015 and 2016
City of York Council
17 SitesHazel Court
West Offices
2 Sites(2012)
276 parking spaces at West Offices
Free City Centre Parking for staff abolished earllier
Findings
Possible correlations: e.g.
Working form home, and
feeling in control of working
0%
10%
20%
30%
40%
50%
60%
Car Car aspassenger
Train Bus Cycle Walk
Emp
loye
d P
op
ula
tio
n
Travel to Work at City of York Council (CYC)
CYC - before
CYC - after
York
England
J. Best
Context
AnalysisMode of travel
Journey to work
Business travel
Non-working travel
Working habits
Timing of the working day
Office- and home-based working
Wider changes
Lifestyle and home base changes
Commencing and ceasing employment
Changing views of the reorganisation over time
Data: Survey
Questions similar to Year 1 & 2 comparisonsNew surveys 2015 and 2016
Data: Interviews
Open ended questions16 interviewees30 minutesNew interviews 2015
AdvantagesInsight into decision makingCapture anecdotal evidence Intentions to move home Staff leaving and joining
References
AECOM. 2012. City of York Council HQ (West
Offices) – Travel Plan.
City of York Council. 2015. www.york.gov.uk
Shires, J. 2014. City of York Council: Workplace
Reorganisation - Initial Survey Findings. Institute for Transport Studies, University of Leeds.
Office for National Statistics. 2013. 2011 Census:
Method of travel to work. Table CT0015.
Acknowledgements
Contact [email protected]
J. Best
Contains Ordnance Survey data © Crown copyright and database right 2015
Central Location
(2014)
(2011)
Jeremy Shires, Supervisor
A study of Public Bike Sharing in Madrid: BiciMAD
What are we hoping to find out and how are we going to do it?
Wait a minute… Why is all of this important? Let’s
Madrid DOES NOT HAVE A CYCLING CULTURE
Quick facts related to cycling in Madrid: • Low modal share of cycling (0.6%)
but high share of walking (36%) and public transport (43%)
• 316 km of bicycle routes (see map) • Hilly topography, up to 200 m of
level difference • Aging society (mean age 43 years) • Government commitment to
promote cycling
Exis\ng cycling infrastructure in Madrid in red and green (Green Ring):
What is cycling in Madrid like?
Loca\on of current BiciMAD sta\ons:
Irene Cobián Mar_n Final Disserta\on 2014-‐2015
MSc Sustainability (Transport) Ins\tute for Transport Studies
University of Leeds
Recent progress:
The piloting was carried out trying to reach different types of
individuals so that the small sample was representative (a student,
an employed person, a retired person, a parent, a tourist…). The
questionnaire was fixed to make it more understandable.
The final version questionnaire was launched on 10th April. It will be
allowed to respondents to send their responses back until 10th May
(a month). At the moment 32 responses have been delivered.
RESEARCH QUESTIONS:
Has the system changed actudes towards biking?
Which are the travel purposes that BiciMAD is preferred for?
Which are s\ll the most important barriers to cycling in
Madrid? How well integrated is
BiciMAD in Madrid’s public transport network?
METHODOLOGY: Informa\on will be collected through a ques\onnaire: • The ques\onnaire is based on the theory of
planned behaviour • It will be piloted and corrected before launching • A snowball technique will be used (online) and
some people will be interviewed • A period of a month will be allowed for
respondents to answer online
What is BiciMAD?
Public Bike Sharing has enable
d bicycles to rise as as
public transport op\on. BiciM
AD is a Public Bike
Sharing System installed in the city of Madrid.
Inaugurated in June 2014, its c
haracteris;cs are:
• Electric-‐power-‐a
ssisted bycicles (pedelecs)
• 123 docking sta;ons with 3,120
racks installed every
300-‐500 m opera;ng 24/7
• High-‐tech kiosks for registra;o
n, pick up/drop off,
payment, account recharge…
• Online applica;ons and mobile apps pr
ovide
informa;on on availability and a
llow for dock
reserva;ons
• Demand responsiveness: discounts
for picking up/
dropping off in high/low availab
ility sta;ons
• Tariffs designed to respect the w
alking share
Parts of the quesionnaire: 1. General ques\ons 2. Actudes 3. Subjec\ve norms 4. Perceived
behavioural control
5. Habits 6. Demographics
Cycling has so many BENEFTIS to offer to society in many different fields!
Figuring out what works to promote cycling and what doesn’t is key in order to design successful measures and achieve these benefits.
NOISE
HEALTH
Economy
Road safety
Landscape invasion
Energy consumption Convenience
POLLUTION�
First impressions are that there is great
concern about safety (great speed of
cars in Madrid) due to the lack of
cycling infrastructure and that
respondents consider the
system to be too expensive.
While they have the potential to solve the problems inherent to conventional drainage
systems, the application of permeable pavements on heavily-trafficked roads poses a
number of challenges.
• The lower structural bearing capacity of the permeable pavement means difficulty handling
the high loads of traffic (MAPC, 2010).
• Loose pavement material as well as brake and tyre dust could accumulate in a way that clogs
the pavement pores (Hunt, 2011).
Conventional Asphalt Pavement Permeable Asphalt Pavement
Images adapted from Marshalls, 2015.
One way is to stabilise the permeable pavement layers with cement or other material.
Stabilisation Permeability Bearing Capacity Layer Depth Cost
•Water is the number one enemy of bituminous
pavements. The reason behind this is the fact that
water infiltrating the pavement layer, mixed with
oxygen, could form reactions that make the bitumen
binder brittle, causing it to strip away and destroy the
pavement (Lambert Bros., 2005).
•Another cause for concern when it comes to water
damage is infiltration into the lower layers of the
pavement, where water may cause structural failure
in expansive soils that are prone to swelling (Elarabi,
2010).
•Traditional design of highway pavements revolve
around the idea of keeping water out (DMRB, 2013),
requiring impermeable pavement binding materials,
such as bitumen, as well as cross-sloping roadways
and gullies and gutters to drain all the water from the
pavement.
• Conventional water drainage systems are not only
expensive to maintain, but recent research shows they
pose environmental threats in that running water
across pavement surfaces carry with them pollutants
and biological contaminants that end up in our rivers
and waterways, poisoning marine life, wildlife as
well as us (Davis and Masten, 2003).
•Permeable pavements allow for the infiltration of
water through the pavement into the subgrade soil
without the need to generate runoff.
1. Davis, M. and Masten, S. 2004. Principles of environmental engineering and science. New
York, NY: McGraw-Hill.
2. Department for Transport. 2013. Design Manual for Roads and Bridges.Volume 4:
Geotechnics and Drainage, Section 2: Drainage. London: Department for Transport.
3. Elarabi, H. 2010. Damage mechanism of expansive soils. Khartoum: University of Khartoum.
• Define and identify the problems underlying the use of
permeable pavements on high traffic roads.
• Address the underlying problems in a way that
optimises performance and costs to ensure an effective
and improved design.
BACKGROUND OBJECTIVES
METHODOLOGY
PERMEABLE PAVEMENTS
APPLICATION OF PERMEABLE PAVEMENTS IN HEAVILY-TRAFFICKED ROADS
Isam Bitar, MSc Transport Planning and Engineering
Institute for Transport Studies. Supervised by Mr David Rockliff
Asphalt Layer
Well-graded
Base
Permeable Asphalt LayerOpen-graded
Base
Well-graded
Sub-base
Subgrade
Open-graded
Sub-base
Literature
Review
Identifying
Problems
Underlying
Reasons
PerformanceCostOther
Factors
Solutions
Based on
Literature
Own
Suggestions
REFERENCES
4. Hunt, W. 2015. Maintaining Permeable Pavements. [Online]. Raleigh, NC: North Carolina
Cooperative Extension. Available from:
http://www.bae.ncsu.edu/stormwater/PublicationFiles/PermPaveMaintenance2011.pdf
5. Lambert Bros. Paving. 2005. Facts about asphalt pavement. Lambertpaving.com [Online].
Available from: http://www.lambertpaving.com/articles.htm#1
6. Marshalls Garden Paving and Driveways, 2015. Drivesett Argent Priora Permeable Block Paving. Marshalls.co.uk. [Online].
Available from: http://www.marshalls.co.uk/homeowners/view-drivesett-argent-priora-permeable-block-paving
7. Metropolitan Area Planning Council (MAPC), 2010. Factsheet # 6: Permeable Paving [Online]. Massachusetts: Metropolitan
Area Planning Council. Available from: http://www.mapc.org/sites/default/files/LID_Fact_Sheet_-_Permeable_Paving.pdf
All links last retrieved 25 April 2015
DEPLOYMENT STRATEGIES OF ELECTRIC VEHICLES IN EUROPE – UK Case Study on Driver AcceptanceResearcher: Hasan TUFAN ([email protected]), MSc. Sustainability (Transport)Supervisor: Dr. Frank Lai ([email protected]); Second Reader: Dr. Samantha Jampson
IntroductionDriving electric vehicles is considered as an important alternative solution toimprove the environmental sustainability of road transport reducing relevantcarbon emissions. Many automotive manufacturers have recently introduceddifferent models of electric vehicles (EV) to the market especially in developedcountries such as European countries.
EU Target: Decreasing the usage of fossil fuel cars by 50% in urban transport by2030 and gradually getting rid of them by 2050.(European Commission,2011)
United Kingdom: The key technology to achieve the targets of emissionreductions for light duty vehicles in UK is electric powertrains. 16% market shareby 2020, 60% market share by 2030, 100% market share by 2040 (ElementEnergy,2013)
BackgroundMany European governments apply policies to deploy more EVs on their roads tobenefit this technology for their future goals in respect to EU framework onenergy consumption, greenhouse gas emissions and dynamic economicenvironment for automotive industry. However, some barriers such as rangeanxiety, maximum speed and performance, purchase price, charging time andshortage of charging locations against the success of these policies.(EU,2012a;Tranet.al.2012)The leading current policy action is the implementation of governmentincentives for wider adoption of EVs in Europe. (Zhang et.al.,2014)
UK incentives cover Plug-in Car and Plug-in Van Grants for the purchase of eligiblecars by 25% of the cost of the vehicle; for vans, up to 20%, Zero-rated car tax;,Zero-rated fuel tax, and the Ultra Low Emission Discount Scheme (ULED) whichexempts EVs from paying the London Congestion Charge. (Next Green Car,2015)
ObjectivesThe key objective of this study is to uderstand the impact of governmentincentives for the deployment of electric vehicles, analyzing the case inUK. This involves in general; Influence of incentives in product related criteria such as price,
charging time and range Their impact on consumer related issues such as age, gender, income
and social status Implications for EU wide policyGaps In IndustryThere are many researches on the effects of barriers on drivers, but alimited answers on interrelationsips of potential solutions are provided.(Lin, 2014)The familiarity of solutions for the adoption of new technologies is animportant concern. (EU, 2012a) Therefore, it is not clear that howincentives affect the familiarity of potential customers for EVs.
Proposed Methodology
Proposed AnalysisAnalysis of the answers of the respondents in questionnaire and focusgroup who are currently driving fossil fuel cars depending on theirperceptions about incentives including following issues:
In what extent the fossil fuel car drivers aware of incentives?
Cross tabulation: Any change on the familiarity level of EVs afterincentives,
Relationship of incentives and other factors
The future of incentives
Expected Contributions and ImplicationsThe success of incentives in UK showed that they might benefit for wideradoption of EVs and changed the perception of people who intend to buya new car.
As a EU member, the similar incentive policies on EV incentives in UKcan be extended to all members of EU in order to deploy moresustainable cars in the roads.
Institute for Transport StudiesFACULTY OF ENVIRONMENT
Research Questions Despite the fact that average distance of daily car travels in UK is
almost 40 km, why range is considered as an excuse for reluctancy andhow incentives can change such perceptions?
In what extent, government incentives change the purchase decision ofEVs, and how did work in UK?
In the future, how long and in what circumstances incentives shouldcontinue in UK?
Source: http://www.edie.net/news/6/Ultra-low-emission-vehicle-SMMT-electric-car-sales-2014/
Alternative FuelVehicle Registrations (2010-2014)
Source: EU, 2012b
Since 2011, the year the incentiveson EV purchases initiated, numberof EVs purchased have increased;the rate of increase between 2013and 2014 was 300.8% in UK, whilethis figure was 40.8% in Germanyand 20.3% in France.(ACEA,2015)
There are many criterion on thedecision of buying EVs like price, fuelcosts, brand, age, gender, educationand income.(Emsenhuber,2012)
Average Distance of Daily Car Travel in European Countries Results
Report of Dissertation
Analysis
Data Cleansing Analysis of Factors
Data Collection
Questionnaire Focus Group
Literature Review
Incentives for EV Purchase DecisionCriteria Relationship of Factors
Poster Presentation Galo Cardenas / Institute for Transport Studies / May 1 / Transport Dissertation / Author: Galo Cardenas / Supervisor Caroline Mullen / Co-supervisor: Giulio Matiolli
GIS Based Accessibility Study of LancashireMuhammed Farhad Rahman | Student no. 200750535 | University of Leeds | 01 May 2015
Background• Accessibility is the ‘extent to which individuals and households can access
day to day services, such as employment, education, healthcare, food stores and town centres’ (DfT, 2012. P2)
• Without suitable access to opportunities an individual’s economic and social welfare can be limited leading to social exclusion
Study area• Population of over 1.4 million (census 2011)• Estimated economic value £23 billion per annum (LEP,
2014. P7)• Contains areas within the 10% most and least deprived in
the country• 80% is classified as rural and 79% of the population live in
urban areas (LCC, 2014)
The number of opportunities at an LSOA* level within specified timethresholds based on weekday journey times by public transport with an arrivaltime by 09.00 *DEFINITION A super output area was ‘designed to improve thereporting of small area statistics’ (ONS n.d.), of which a LSOA is the smallestoutput area.
Objectives• Understand the role of accessibility within local government and the
limitation of LTP2• Clearly define measurable and non measurable barriers to accessibility
across different domains• Quantify origin accessibility within the study area by undertaking a strategic
mapping exercise and make policy recommendations based on results
Local Transport Plan 2 (LTP2)
Methodology
Limitations• Accessibility is multi‐faceted; a single
accessibility score does not reflect this• Transport ‐ does not factor in car
ownership• Land use ‐ limits users to public transport
despite opportunities being accessiblevia walking or cycling resulting in aninappropriate land use measure
• Socioeconomic ‐ does not take intoconsider 'deprived' individuals may lackthe resource to access public transport interms of finance or limited mobility as aresult of health problems or limitedtravel horizon
• Arc View GIS will be used as it is a powerful mapping analysis tool enablingdata to be inputted at the required geographical scale (LSOA level)
• Accessibility is separated into domains enabling in‐depth analysis throughindividual domain scores [please note each domain produces average scoresat an LSOA level and does not reflect an individual’s circumstance]Transport – the availability of transport
• Car ownership (census 2011) – calculate the proportion of homes that have at least 1 car or van
• Availability of peak time high frequency bus (at least 6 buses an hour) – acceptable walking distance 400m to bus stop
Land use – the number of opportunities within time threshold
• Use LTP2 time thresholds to calculate the number of opportunities within an LSOA using any mode of travel other than a car or vanSocioeconomic – interaction of social and
economic factors• Index of multiple deprivation (IMD) score will
be used as an indicator• IMD provides ‘a relative measure of
deprivation at small area level across England’ (Department for communities and local government. n.d.).
• ‘Income effects and other indices of social disadvantage have a significant influence on travel behaviours' (Lucas K, et .al. n.d. P14)
Accessibility score – overall accessibility ranking• Measure of the transport, land use and socioeconomic domains combined• A relative measure of accessibility is produced i.e. a score of 80 is not twice
as accessible as 40• An LSOA can be characterised as highly accessible relative to other areas,
however, individuals within the LSOA may still face accessibility issues
Example of preliminary results
Policy recommendation
A low transport score means….• Increase bus frequency if appropriate• Enable community transport if applicable• Encourage car sharing
Analysis• Despite 005C being classified as rural, at
a LSOA level it is deemed more accessible than 007C with accessibility scores of 179.44 and 176.25 respectively
• 005C – with a land use score of 14.3, the physical separation of opportunities is the main factor limiting accessibility
• 007C – with a socioeconomic score of 19.92, deprivation is the main factor limiting accessibility
Project limitationFollowing domains are not included
A low land use score means….• Increase mixed use developments• Increase density of opportunities through the planning process and
planning policies (e.g. local plan)CAUTION increasing density in the urban fringe 'can spoil the amenities thaturban fringe resident's desire' (Litman T, 2015. P26).
A low socioeconomic score means....• Make travel more affordable if applicable• Increase travel horizon (linked with education, health, living conditions
etc.) – further study necessary
Information• A lack of information has a direct link on an individual’s travel mode and
ability to travel• People ‘tend to avoid modes where they feel they do not have good
enough route knowledge' (TfL, 2009. P15). • Difficult to measure how much information is needed for a location to be
accessible
Perception• Perception is 'the way in which something is regarded, understood or
interpreted’ (Oxford dictionary).• Our perception of a journey may limit our travel horizon• Requires large data collection exercise – very costly
ReferencesDepartment for communities and local government n.d. English Indices of Deprivation 2010 http://data.gov.uk/dataset/index‐of‐multiple‐deprivation dateaccessed 21.04.15Department for Transport (DfT). Accessibility statistics guidance V1.2. July 2012. P2Geograph. Photograph every grid square. Portland Street Accrington. http://www.geograph.org.uk/photo/2311769Lancashire County Council (LCC). Local Transport Plan 2 (LTP2), 2006. P355Lancashire County Council (LCC).Rural urban definition for small area geographies. 2014http://www3.lancashire.gov.uk/corporate/web/?siteid=6116&pageid=43246&e=e date accessed 21.04.15Lancashire Enterprise Partnership (LEP). A Gorwth Deal for the Arc of Prosperity March 2014. P7Office of National Statistics (ONS). Super Output Area (SOA). n.d. http://www.ons.gov.uk/ons/guide‐method/geography/beginner‐s‐guide/census/super‐output‐areas‐‐soas‐/index.html date accessed 21.04.15Litman T, Evaluating accessibility for transportation planning. Measuring people’s ability to reach desired goods and activities. Victoria Transport Institute.January 2015. P26Lucas K, Bates J, Moore J, Carrasco J & Antonio J. Modelling the relationship between travel behaviours and social disadvantage. n.d. P14Morris K. Research into travel horizons and its subsequent influence on accessibility planning and demand responsive transport strategies in GreaterManchester. Halcrow Group Limited 2006. P1Oxford dictionary http://www.oxforddictionaries.com/definition/english/perception date accessed 21.04.15The Marmot Review, Fair Society, Healthy Lives. Strategic Review of Health Inequalities (2010). P134Transport for London (TfL) Older people’s experience of travelling in London. Mayor of London. 2009. P15
Risks• Results are only as reliable as the data inputted• Accessibility scores produced are an average of the LSOA and is not a
reflection on whether individuals face accessibility issues
0102030405060708090100
LSOA domain scoring
Ribble Valley 005C Burnley 007C
Source: LCC, 2006. P355
LTP2: Accessibility mapping exercise
Burnley bus station
Portland Street, Accrington
Source: Geograph
Policies• Lancashire Highways and Transport Masterplans have stated a need for an
accessibility study• The Marmot Review states ‘fully integrate the planning, transport, housing,
environmental and health systems to address social determinants of health in each locality’ (The Marmot Review, 2010, P134)
Recommendation will vary depending ondomain score, geography and individualcircumstance
0
20
40
60
80
100
Car ownership Access to highfrequency bus
Transport domain
Ribble Valley 005C Burnley 007C
For normalization, Z score= (Raw Score of each MSOA- Mean Raw Score of whole District)/Standard deviation of Raw Score of Whole District
WI= (2*CI) +(HDI +FARI)+ EntI + EF +PI
GIS Model Builder:
1. BACKGROUND AND SCOPE
To select indices for calculating a walkability index from existing literatures
To test the applicability of this index in two case study areas of UK (Leeds and York)
To make recommendations for more general application of the method in UK and other places
Scope: This study will help to see the applicability of such method in other cities of UK from the comparative analysis of the cities. Spatial aggregation is also possible, but not in scope of this study.
Walkability defines the extent to which the built environment is walking friendly. The role of built environment is utmost important in this case (NZ Transport Agency, 2009).
Creating walkability index is such a method where indices can be developed both subjectively (Walkonomics.com, Walkscore.com) and objectively (GIS) to define the relationship (Leslie et al., 2007; Cervero, R., 2005; Agampatian, R. 2014).
PERS is a qualitative walking audit tool but for route based system (TRL, 2009). IPEN developed a method where four partial indices were created which then combined to get a final composite (area wide) score (Dobesova, Z. and Krivka, T. 2012). This method is widely used in North American cities but there are very few applications in UK .
Considering all the above situation, this study intends to create a walkability index from the publicly available GIS data for the cities of UK.
An Automatically Generated Area Wide Walkability Index For UK Cities Based On Existing GIS Data
4. METHODOLOGY
3. STUDY AREA AND DATA SOURCE
Step 1: Calculating 5 partial/raw parameter indices
1. Connectivity index:
Directness of the pathway between households, shops and places of employment
CI = Number of intersections of roads/ square km of urban units
5. Proximity
Describes number and variety of destinations within a specified distance (buffer) of any location.
Creating points of interest destinations (eg. Parks).
Creating buffers (< 1 km)
Weighting these buffer layers based on importance
4. Environmental
friendliness:
Important for Comfort;
Cleanliness and Safety.
EF = sidewalk coverage
in m2/street-roadbed
coverage in m2
2. Density:
Household density:
HDI = No. of HHs/ sq km residential area.
Commercial Density:
FARI = area of CBs/area of CLs
Middle Layer Super Output Area (MSOA): minimum 5,000 population (an average of 7,700) and 2,000 households (an average of 3,200)of Leeds and York (National Statistics, 2011).
Data sources:
1. Edina Digimap website (digimap.edina.ac.uk)
2. National Statistics website (ons.gov.uk)
3. UK data service: census support website (census.ukdataservice.ac.uk)
4. OpenStreet Map website (openstreetmap.org)
5. Google Earth (earth.google.com)
A map showing which areas are walking friendly and which are not, based on WI.
Will help to understand the walking condition of UK based on the physical environment.
Will help decision makers to take proper interventions regarding investment on the pedestrian facilities.
5. INTENDED RESULTS
2. OBJECTIVES AND SCOPE
6. LIMITATIONS 7. REFERENCES
Agampatian, R. 2014. Using GIS to measure walkability: A Case study in New York City. Unpublished Thesis Report. [Online]. [Accessed on 30 January, 2015]. [Available at http://www.diva-portal.se/smash/get/diva2:715646/FULLTEXT01.pdf]
Cervero, R., 2005. Accessible Cities and Regions: A Framework for Sustainable Transport and Urbanism in the 21st Century. UC Berkeley Center for Future Urban Transport: A Volvo Center of Excellence. Institute of Transportation Studies (UCB), UC Berkeley. [Online]. [Accessed on 30 January, 2015]/. [Available at: http://escholarship.org/uc/item/27g2q0cx]
Dobesova, Z. and Krivka, T. 2012. Walkability Index in the Urban Planning: A Case Study in Olomouc City. Advances in Spatial Planning. Dr Jaroslav Burian (Ed.). ISBN: 978-953-51-0377-6.
Leslie, E., Coffee, N., Frank, L., Owen, N., Baumane, A. and Hugo. G., 2007. Walkability of local communities: Using geographic information systems to objectively assess relevant environmental attributes. Health & Place. (13) pp: 111–122.
NZ Transport Agency, 2009. Pedestrian planning and design guide. [Online]. [Accessed on 24 January, 2015]. Available at http://www.nzta.govt.nz/resources/pedestrian-planning-guide/docs/pedestrian-planning-guide.pdf
TRL, 2009. Pedestrian Environment Review Software. [Online]. [Accessed on 24 January 2015]. Available at https://trlsoftware.co.uk/products/street_auditing/pers
Step 2: Final Walkability Index:
The current GIS database are not readily available and incomplete. The missing gaps will be filled in manually by the researcher from Google Earth source.
Some of the parameters cannot be incorporated for unavailability of recent data like: traffic flow, speed etc.
This study is based on objectively measurable data. Subjective data (such as people perception about walkability) is not considered.
FARZANA KHATUN (Student No: 200890976), MSc Transport Planning and the Environment- May 2015
Supervisor: Dr ASTRID GÜHNEMANN, Senior Lecturer, ITS, University of Leeds
MSOA boundaries: Leeds
MSOA boundaries: York
Weighted
Overlay
Connectivity
Index
Density
Index
Diversity
Index
Environmental Friendliness
Index
Household
Density
Commercial
Density
Walkability
Index
Proximity Index
INSTITUTE FOR TRANSPORT STUDIES
3. Diversity:
Spatial arrangement of landuse
𝐸𝑛𝑡𝐼 =− [(𝑃𝑖
𝑘𝑖=1 ) .(𝑙𝑛𝑃𝑖 )]
𝑙𝑛𝑘
k is the category of land use;
p is the proportion of the land area devoted to a specific land use;
N is the number of land use
Investigating the Temporal Transferability of Vehicle Ownership Models: A case study of the Dhaka Metropolitan Area, Bangladesh.
Flavia Anyiko.| Dr. Charisma Choudhury (Supervisor) | Dr. Thijs Dekker ( Second Reader)
1. To develop vehicle ownership models and test for temporal transferability
2. To investigate the effect of model structure on temporal transferability
3. To compare the performance of potential methods in improving temporal transferability.
BACKGROUND DATA AND SCOPE
OBJECTIVES
Growing use and ownership of private
vehicles in developing countries.
Accurate prediction of vehicle growth
important for policy aimed at control and
management
Modelling of vehicle ownership costly. Previous models
used without updating.
Transport conditions in developed and
developing countries are significantly
different.
Research on the temporal transferability of vehicle ownership models in the context of developing
countries.
MODEL STRUCTURE
Previously used models from literature include; • MNL, ORL, NL This research will estimate relationship between vehicle ownership and independent variables (Income, HH size, Licenced drivers,..etc)
Model Structure 1: MNL model
Model structure 2: NL model
None Cars Motorcycles
Bicycles
None
Car MC BC
Cars MC BC
1 2+ 1 2+ 1 2+
Estimate Vehicle ownership models using 2005 data
Output: subset of models with goodness of fit
Test Transferability of estimated models.
Re-estimate models using 2010 data. Conduct tranferability test, comparing models from two data sets
Model Updating
Update models by bayesian method, combined transfer estimation, joint context estimation. Repeat transferability tests to compare performance of updating method and model structure
METHODOLOGY Preliminary Findings
Model Structure 1 Variables that positively impact vehicle ownership; HH size, licenced drivers, workers per HH. Outstanding: No meaningful results yet to explain r/ship between income and vehicle ownership
CHALLENGES
Many zeros in the data. Will selected model structures correctly estimate the relationship? Differences in 2005 and 2010 datasets. Different sample size
The Issues
• To examine critically the current urban railway regulatory framework
• To develop set of recommendations for
amendment to the current framework
• What are the different structures used world wide for the regulation and organization of railways?
• To what extent is the separation of management
and accounting in the delivery of both railway infrastructure and railway operations appropriate in the study area?
• To what extent are the financing arrangements
supportive of the regulatory, management and accounting structure of railways in study area?
• What amendments to the existing regulatory,
management, accounting and financing for railways in the study area are to be recommended?
2. Research Objectives 4. Methodology
3. Research Questions
Literature review Review on regulatory
framework world wide
Review on regulatory framework in Jakarta
Determine the criteria & method in assessing the
framework
Data Collection
Analysis
Conclusions and recommendations
Appropriateness of the Regulatory Framework of Urban Railway in Jakarta and its Greater Area
Classification of framework & Selection of cities to be
benchmarked
Main Structures Identified
• Integration model • Holding model • Separation model
Qualitative Analysis Benchmarking
5. Preliminary Findings
Main Institutional Arrangements
identified
• Public Monopoly • Competition in the market • Competition for the market
Assessment Criteria
Identified
• Efficiency • Cost • Level of Services
Potential Risk:
• Unavailability of data
• Commercial-in-confidence data which can not be published
• Inconsistency in data collection methodology or definition of data between different sources
• Stakeholders might refuse to be interviewed
• Bias in qualitative research
Primary Data: Video call and email Interviews with relevant stakeholders (transport authority, train operating company, line ministries)
Secondary Data: • Train operators &
infrastructure’s annual & performance reports,
• Railway statistic report (Eurostat, OECD & Directory etc.)
• Consultancy report (World Bank, JICA etc.)
Indonesian Government (Policy Maker)
Transport Authority (Technical Auditor)
Service Provider (State Owned Companies)
Private Contractors
KCJ MRT-J
Ministry of State Owned Enterprises (Financial Auditor)
Track Access Charge
Infrastructure O & M fees
Subsidy
Business Contract
Current Urban Railway Regulatory Framework
• Massive vehicular movements & road based congestions
Tokyo 37.2
Jakarta 26.7
New York 20.7
Sao Paolo 20.6
World’s City Population (2013, in millions)
• One of the most densely populated mega cities • High rate of Vehicle growth & motorization
Source: World Bank (2014)
25
30
35
40
45
50
20
04
20
06
20
08
20
10
20
12
20
14
Ro
ad
Are
a
(mill
ion
m2
)
Year
Vehicle Growth related to Roads Development in Jakarta
Road
Vehicle
4 wheel vehicle (x 1000)
3.300
3.000
2.700
2.400
2.100
1.800
Source: Provincial Government of DKI Jakarta (2012)
The Plans
1. Background Context
• Increasing public transport modal share from 20% to 60%
• Focus on rail system : expanding current lines, constructing new lines, reforming regulatory framework
• Rudimentary rail system (commuter lines) – total of 235 km track length
KCJ manage infrastructure and operate trains for the commuter lines. MRT-J will manage and operate trains for MRT lines
Total Area Jakarta & Its Greater Area: 6932 Km2
Source: Lubis (2008)
Type Variables Justification Collection
Demographic Gender, age, employability, income, Socio-economic status Personal background
characteristics household role and size, driving license held.
Physical and Health condition, daily behavioural Individual physical and psychological Instrumental activities of daily
psychological capacity condition living (IADL’s)
Travel Trip generation, origin and destination, time and space constraints and activity Travel diary and Personal
behaviour purpose, trip time and duration, travel pattern , activity type and place backgroundbehaviour purpose, trip time and duration, travel pattern , activity type and place background
distance, modal choice, modal owned
In this study, Travel time ratio is always expected to be within range from 0 to 1, therefore, a generalised linear model (GLM) will be
adopted. The exponential-family distributions should be binomial and link function is logit since constraint of TTR is within 0 to 1.
Alsnih, R. and Hensher, D. (2003) The mobility and accessibility expectations of seniors in an ageing population. Transprtation Research Part A 37: 903-913Ben-Akiva, M. and J.L. Bowman, Integration of an Activity-based Model System and a Residential Location Model. Urban Studies, 1998. 35(7): p. 1131-1153.Dijst, M.J. (1995) Het elliptisch leven: actieruimte als integrale maat voor bereik en mobiliteit –modelontwikkeling met als voorbeeld tweeverdieners met kinderen in Houten en Utrecht. Utrecht/Delft, Koninklijk Nederlands Aardrijkskundig Ge-nootschap/Faculteit Bouwkunde, TU-Delft (doctorate thesis, in Dutch with extensive summary in English).Kwan, M.-P. (1998) Space-time and integral measures of individual accessibility: a comparative analysis using a point-based framework. Geographical Analysis 30 (3): 191-216Newbold, K., Scott, D., Spinney, J., Kanaroglou, P., and Páez, A. (2005) Travel behavior within Canada’s older population: a cohort analysis. Journal of Transport Geography 13: 340-351.Pas, E. I. (1985). State of the art and research opportunities in travel demand: Another perspective. Transportation Research Part A: General, 19(5–6), 460-464. doi: http://dx.doi.org/10.1016/0191-2607(85)90048-2Rosenbloom, S. (2001) Sustainability and automobility among the elderly: An international assessment. Transportation 28: 375–408.Rosenbloom, S. (2001) Sustainability and automobility among the elderly: An international assessment. Transportation 28: 375–408.Schmöcker, J., Quddus, M., Noland, R., Bell, M., (2005) Estimating trip generation of elderly and disabled people: an analysis of London data. In: Proceedings of the 84th Annual Meeting of the Transportation Research BoardSusilo, Y.O. and Dijst, M. (2009) How far is too far? Travel time ratios for activity participations in the Netherlands. Transportation Research Record 2134: 89-98.Wen, C.-H. and F. Koppelman, A conceptual and methodological framework for the generation of activity-travel patterns. Transportation, 2000. 27(1): p. 5-23.
Huang Ding–Jhong
Supervised by Dr. Frank Lai
M.Sc. Transport Planning & Environment
RESEARCH POSTER PRESENTATION DESIGN © 2012
www.PosterPresentations.com
•Toreviewtrafficmicro‐simulationstudiesofscrambleintersections•AssesstheperformanceoftheScramblejunctionoptionincomparisonwiththecurrentsignalisedjunctionundervarioustrafficflowsandpedestriandemandconditions.•SuggestgeneralguidelinescriteriaforScramblejunctionsmicrosimulations.
TheconceptofscrambleintersectionwasintroducedinVancouverandKansasCityinthe1940stheninDenverinthe1950s.Japanhasoverthreehundredofscramblejunctions,thisincludestheworld’sheavilypedestrianscramble,atHachiko Square,Shibuya,Tokyo.InUK,Balhamcrossingwasintroducedfirstin2005thentheOxfordCircusin2009.However,inUK,littleguidanceisgivenbytheDfT ondeterminingwhetherdiagonalcrossingshouldbeusedasopposedtomoretraditionallayout(Greenwood,2012).
Introduction
TheObjectives
Example:OxfordCircus
Methodology
LegionforAimsun model
CaseStudyAreaThisresearchiscarriedoutatthejunctionalongA660Otley RoadandB6157inHeadingley.ThestudyintersectionislocatedatthecoreoftheHeadingley areawhichhashighpercentageofstudentaccommodation,bars,shopsandthevenueforLeedsRhinosRLFCandLeedsCarnegie.ItisalongthebusyA660roadwhichconnectsLeedsCityandnorthernareas.Thisjunctioncarrieshighlocalvehicleandpedestriantraffic.Figure1showstheGooglepictureoftheproposedjunction.
ReferencesGoogleMaps.2015.A660/B6157junction[Online].[Accessed14April2015].Availablefrom:www.google.co.uk/maps/@53.821135,‐1.577556,3a,75y,340.05h,70.01t/data=!3m4!1e1!3m2!1sbnxcuBjzgwMYwZDIZddxjg!2e0Greenwood,C2012.ImageofOxfordCircusscheme.[Online].[Accessed14April2015].Availablefrom:http://www.atkinsglobal.com/~/media/Files/A/Atkins‐Global/Attachments/sectors/roads/library‐docs/technical‐journal‐4/scrambled‐pedestrian‐crossings‐at‐signal‐controlled‐junctions‐a‐case‐study.pdfBradshaw,A.2015.Proposedfoodstoremodelling.[Online].[Accessedon14April2015].Availablefrom:http://www.its‐ukreview.org/a‐model‐approach‐to‐transport‐assessment/LeedsCityCouncil.2014.PersonalinjuryaccidentsinLeeds:Sitesforconcern.[Online].[Accessedon26April2015].Availablefrom:http://www.leeds.gov.uk/docs/Sites%20for%20concern%202014.pdfHCM.2000.Transportationresearchboard.NationalResearchCouncil,Washington,DC.
Supervisor: Dr James Tate; 2nd Reader : Hamish Jamson
Clifford Zwomuya: MSc (Eng) Transport Planning and Engineering
Assessing the performance of a Scramble intersection using microscopic traffic and pedestrian simulation tools
Figure2:ViewofOxfordCircus(Source:Greenwood)
Figure1:Optionjunction(Source:GoogleMaps)
GeometricRepresentation•Globalparameters•Localparameters•DXFfilefromGIS•Trafficparameters•Trafficsignals
TheModelScenario1:
SignalisedOptionScenario2:
ScrambleOption
Comparison•Junctionperformance
•Safetylevel
BestScenario
Table1:LevelofService(LoS)criteria(Source:HCM2000)
Figure3:LegionforAimsunmodel(source:Bradshaw)
ModelcodingConfiguration
EstimationofOrigin‐DestinationMatrixTraffic flows
•Pedestriancounts•Pedestriancrossinglocations•Sidewalkcharacteristics
DataInput
ModelCalibrationandQualitycontrol
PedestrianandTrafficModelling
GEH Analysis:ComparisonwiththeDfT
BaseModelFormulation
1. LiteratureReviewReviewinganddeterminationofrelevantliterature
2.DataCollectionandPreparationRelevantdata,cleaningandorganisingdata
3.DataAnalysisUseofLEGIONofAIMSUN
4.InterpretationofResultsEvaluatingtherelevancyofresults
AIMSUN:CalibratedandValidatedfor2014demandlevels
Veh travelspeedLoS onurbanroadsPedestrianLoS criteriaforsignalised
delay
LoS 30mph LoS Delay(s)Likelihoodofpednoncompliance
A >25Motoristsdrivingatdesiredspeed
A <10Low
B 19‐ 25 Desiredspeedsignificant B 10‐ 20
C 13‐ 25Flows stablebutsusceptibletocongestion
C 20‐ 30Moderate
D 9‐ 13 Unstabletrafficflows D 30‐ 40
E 7‐ 9Unstableanddifficulttopredict
E 40‐ 60 High
F ≤7 Heavilycongested F ≥60 Veryhigh
Year Slight Serious Fatal Total2009 2 0 0 22010 1 1 0 22011 2 0 0 22012 4 2 0 62013 2 1 0 3Total 11 4 0 15
Table2:Thestudyarea’saccidentanalysis(Source:LeedsCityCouncil)
Safety:Dependsonusercompliancetosignalindications;Compliancerestsonperceivedfairness
TheLevelofservice(LoS):Concernedwiththequalityofserviceprovidedbytheroadjunction
0
20
40
60
80
100
120
140
160
180
2009 2010 2011 2012 2013
Num
ber of acciden
ts
Year
slight Serious Fatal
Figure4:AccidentsrecordedinLeeds
Estimating the Marginal Cost of Rail Infrastructure Usage in Britain: An Econometric ApproachBy Christophe J. W. Speth Supervised by Andrew S. J. Smith
A very unique model of railway organisation in Britain:
- Vertical separation between network management (Network Rail) and train operations (28 TOCs)
- Horizontal separation between train operating companies, mainly on a geographic basis
- This is not current practice in other European countries (Belgium, Germany and Northern Ireland)
Hence the need to set up track access charges at the right level:
- Variable access charges should reflect the marginal cost of running extra traffic on the network
- The objective of this work is to estimate the marginal cost of maintenance with respect to traffic
- The full marginal cost of running traffic on the network should also take renewals, congestion and
environmental effects into account
Different methods to measure marginal cost:- Engineering approach (bottom-up)
- Cost allocation approach (top-down)- Econometric approach (top-down)
Methodology:- Following Wheat and Smith (2008), and using econometric methods, estimation
of a cost function:
𝑚_𝑐𝑜𝑠𝑡𝑠𝑖 = 𝑓 𝑡𝑟𝑎𝑓𝑓𝑖𝑐𝑖 , 𝑖𝑛𝑓𝑟𝑎_𝑐𝑖 , 𝑖𝑛𝑝𝑢𝑡_𝑝𝑟𝑖𝑐𝑒𝑠𝑖
- Level of disaggregation: MDU or route- If possible, use of a panel (of at least 5 years)
- Otherwise, use of a cross-section (only 1 year)
Data:- Data on traffic (and possibly input prices) to be provided by Network Rail?
- Data on maintenance expenditure available in Regulatory Financial Statements (Network Rail, 2014a)
- Data on infrastructure characteristics in Annual Return (Network Rail, 2014b)
Policy implications and results:- Are the variable access charges set too low in Britain?
- Cost elasticity findings may help to compare results with similar studies- How has the situation evolved since the work of Wheat and Smith (2008)?
…
References• Abrantes, P., Wheat, P., Iwnicki, S., Nash, C., Smith, A.S.J., 2007. Review of Rail Track Cost Allocation Studies for Deliverable 1 of CATRIN.• Kennedy, J., Smith, A.S.J., 2004. Assessing the Efficient Cost of Sustaining Britain’s Rail Network: Perspectives Based on Zonal Comparisons. J.
Transp. Econ. Policy 38, 157–190.• Link, H., Stuhlemmer, A., Haraldsson, M., Abrantes, P., Wheat, P., Iwnicki, S., Nash, C., Smith, A.S.J., 2008. Cost Allocation Practices in the
European Transport Sector.• Network Rail, 2014a. 2014 Regulatory Financial Statements.• Network Rail, 2014b. Annual Return 2014.• Smith, A.S.J., Kaushal, A., Odolinski, K., Iwnicki, S., Wheat, P., 2014. Developing Improved Understanding of the Relative Cost of Damage
Mechanisms through Integrating Engineering Simulation and Statistical Modelling Approaches.• Wheat, P., Smith, A.S.J., 2008. Assessing the Marginal Infrastructure Maintenance Wear and Tear Costs for Britain’s Railway Network. J. Transp.
Econ. Policy 42, 189–224.
Image Credits• http://www.londonmidlandparking.com/images/lm-logo.jpg• http://www.trackandtrain.org.uk/wp-content/uploads/2012/01/trans-pennine-express.png• http://www.petsallowed.co.uk/images/arrivawales.gif• https://twitter.com/networkrail• http://referentiel.nouvelobs.com/file/5153596.jpg• http://www.trimble.com/rail/images/railwayTrolley_imageLR.jpg• http://www.networkrail.co.uk/aspx/10451.aspx
1.0 Aims and objectives
The research aims to investigate the maintenance of local roads in England, identifying areas that need the implementation of more efficient and sustainable policies and practises.
This investigation will follow the Objectives stated below:
I. Identify and assess existing literature on road maintenance regimes, noting the best practices and policies necessary for efficient and sustainable delivery of road maintenance.
I. Asses the road maintenance regime employed by the local authorities in England.
I. Identify the areas that can be improved in the regimes in England and hence, recommend the most suitable efficient and sustainable practises and policies to those areas.
2.0 Context and Context Background
2.1 Introduction
• In most countries, an efficient road transport system is seen as a critical pre‐condition for general economic development (Robinson, Danielson & Snaith, 1998).
• The Department for Transport and Highways Agency (2014) see the strategic and local road network as England’s “most highly valued infrastructure asset” and admit that maintaining it is vital for the economy and also the social well being of individuals.
• Road user benefits gotten from road improvements include improved access, comfort, speed and safety. Vehicle operating costs are lowered as well (Robinson et al, 1998).
• To sustain those benefits, a well planned maintenance programme must be followed (Robinson et al, 1998).
• Lack of routine and periodic maintenance results in high direct and indirect costs (Robinson et al, 1998).
• With the current spending cuts (Dft & HA, 2014) by the government and the inflation of material costs (Dft & HA, 2014), cost‐effective maintenance regime has to be implemented
3.0 Research Questions• What are the best practices & policies of successful & effective
road maintenance regimes?
• What maintenance regime is used in England and why?
• How could suitable efficient and sustainable improvements be made to the regime in use?
2.2 Key Findings on Road Maintenance in England
Fig 1: Estimated value of England’s roads, miles in England’s road network and maintenance spend 2013/2014 respectively (Dft & HA, 2014).
Fig. 2: Key data on maintenance by local authorities (AIA, 2015)
4.0 Research Methodology
Student Number: 200910126Poster Board: 7Course: Msc(Eng) TP & Eng.
344bn 187000 4.2bn
6.0 Data and risks
• Data sources so far: Government documents, documents from international organizations, textbooks, ALARM survey.
• Other data sources include National transport survey, data from local authorities.
• The risks in conducting this research include:
I. Lack of response.
II. Accidents when travelling.
III. Lack of relevant data.
Write up the findings from the researchWrite up the findings from the research
Present final results Present final results
Analyze collected dataAnalyze collected data
Conduct interviews/Collect relevant secondary dataConduct interviews/Collect relevant secondary data
Review relevant literatureReview relevant literature
Establish Objectives and research questionsEstablish Objectives and research questions
5.0 Scope of researchThis research is to cover road maintenance by the local authorities in England. The interview will be conducted on 6 – 8 local authorities, with scope for more local authorities of possible. Ideally half of the local authorities interviewed are to have successful maintenance regime and the other half, unsuccessful ones.
7.0 ReferencesRobinson, R. Danielson, U. & Snaith, M. (1998). Road maintenance management: Concepts and Systems. Basingstoke and London. Macmillan Press LTD.
Department for Transport and Highways Agency. (2014). Managing strategic infrastructures: Roads (Online). [Accessed on 24/04/15]. Available from http://www.nao.org.uk/wp-content/uploads/2015/06/Maintaining-Strategic-Infrastructure-Roads.pdf
Asphalt industry Alliance. (2015). Annual Local Authority Road Maintenance Survey 2015 (Online). [Accessed on 30/04/15]. Available from http://www.asphaltindustryalliance.com/images/library/files/ALARM%202015/ALARM_survey_2015.pdf
Smartphone impact on college pedestrians while crossing street intersection at Leeds University
Background Objec0ves
Methodology
Scope of the research Chen and Katz (2009): 92% young adult in the UK were possess a mobile phone, become addicted and daily needs in their lives
Hat$ield and Murphy (2006): The usual pedestrian casualties most happen when the pedestrian crossing the street, which also including the intersection
Schwebel et al (2012): Mobile phone or any other distraction such as listening music, conversation and eating gives higher risk while crossing the street
Bungum et al (2005): The road or intersections near campus are more dangerous compared not in campus site as were the pedestrian frequently did not obey the traf$ic signalized due to running on time
This study is more focused on pedestrian behaviors that using a mobile phone while crossing the signalized intersection on campus circumstances. To have better understanding the role of impact mobile phone and any distraction activities among young adult pedestrian To compare the crossing safety between pedestrian using mobile phone and not using To compare the r e su l t be tween observa t i on method and v i r tua l environment method
Research Ques0on Is mobile phone use increase or decrease the cautionary behavior? Is Real and Virtual Environment are the same?
This study will focus on pedestrian at Leeds University intersection among campus circumstances
National Road Traf$ic Survey (2014): In 2013, there are 12,304 of pedestrians casualt ies , 200 were ki l led , which categorized by a group age youth or young adult in Great Britain.
Observation: Weekday 2/2 h period Place: three different intersection near Leeds University (represent most common used crossing site and due to heavy pedestrian traf$ic)
Analysis and Discussion
Pilot Observation: determine cautionary measurement and pedestrian traf$ic time
Figure 1
Figure 2
Figure 3
The data will collected, processed statistically and will presented by texts charts and tables. Then, a brief discussion will reported while try to answer the research question and reach to conclusion
Supervisor: Dr. Frank Lai Ciptaghani Antasaputra, Msc Transport Planning
Design: time matched control – observer recorded all pedestrian using the mobile phone, at the same passing time, recorded who not using
Walker et al (2012): there are no difference between mobile phone user and not trough Virtual Environment
Biomass Collection
Transport StorageEnergy
ConversionPellets
Distribution
BIOMASS-TO-BIOENERGY SUPPLY CHAINDeveloping Strategies for Carbon Reduction
Antonia Thanou Supervisor: Anthony Whiteing
BACKGROUND• By 2050, EU leaders have to reduce Europe’s GHG
emissions by 80-95% compared to 1990 levels (IPCC, 2013)
• By 2020, Directive 2009/28/EC requires that at least 20% of energy consumption in the EU should produced by renewable energy sources
• Biomass is a renewable energy source that could make a larger contribution in the reduction of GHG emissions in terms of electricity generation (Evan et al., 2010)
AIM OF THE STUDY• Exploration of the supply chain of biomass from
agricultural-derived sources in Greece, focusing on the distribution and logistical processes:
Transportation, Storage, and Transhipment
• To what extent is biomass for electricity an attractive option for climate change mitigation in the energy sector?
WHY GREECE?• A big percentage of the available biomass remains
unused
• There is a potential to improve its position in the global pellet market
• Increasing necessity for renewable energy due to the high fossil fuel prices and environmental concerns
OBJECTIVES• Investigate the Greek source of biomass material and its
location• Identify the distribution channel and the foreign markets
that the Greek pellets-industry exports to• Mapping of the supply chain, including the stages of
transport and storage• Evaluate ways in which that particular supply chain could
be improved so as to mitigate GHG emissions
METHODOLOGY & DATA COLLECTION
Literature Review
•Deeper understanding of biomass supply chains
•How the use of biomass can contribute to climate change
Data Collection
•Face-to-face interviews from three Greek pellets manufacturers
•Academic papers on biomass logistics
Supply Chain Mapping
•Accurate identification of the stages and processes in the supply chain
Estimate
GHG emissions
•Calculation of the energy inputs to the system and mass of carbon emitted
References Available: http://biomass-supply-chain.simplesite.com/http://www.ecosmartsolutionsuk.com/
http://www.bbc.co.uk/news/science-environment
http://www.alfapellet.gr/https://www.google.co.uk/maps
Poster template by ResearchPosters.co.za
THE ROLE OF TRANSPORT IN CITY COMPETITIVENESS:
DOES TRANSPORT INVESTMENT MATTER?
CASE STUDY OF ACCRA AND TAMALE – GHANA
SUPERVISOR: Dr. James Laird
1. General Introduction 4. Scope at a Glance 7. Methodology
2. Study Aim and Objectives 5. Development Indicators 8. Primary Data Collection Sources
3. Quick Read about Transport in Ghana 6. The Major Transport Sectors 9. Data Analytical Method
• The transport sector accounts for approximately 9
percent of GDP;
• About 944 kilometers of railway lines and 60,000
kilometers of road network consisting of 20,500
kilometers of trunk roads, 34,000 kilometers of feeder
roads and over 5,500 kilometers of urban roads;
• Ghana has one international airport in Accra (KIA),
and 8 regional airports and airstrips throughout the
country; and
• Road transport remains the predominant mode of
transportation and accounts for 94 percent of freight
and 97 percent of all traffic movement in the country.
AimTo ascertain how transport investment can influencecity competitiveness: Whether transport decision-makers consider investment in transportinfrastructure as having greater influence ondevelopment in Accra/Tamale.
Objectives•To understand the meaning and nature of citycompetitiveness in Accra and Tamale; and
•To identify the specific roles of transportinfrastructure investment in the competitiveness ofAccra and Tamale.
Transport & Connectivity
Presented By: Alhassan Siiba MSc. Transport Planning Student ID: 200861516
University of Leeds, Institute for Transport Studies, UK
TRANSPORT
INVESTMENT
Genearalised transport cost
reduction Accessibility and proximity
Increase economic
productivity
& growth
Improvement in living
standards
& well-being
Economic cluster:
Agglomeration benefits
CITY COMPETITIVENESS
Source: Adapted from: Venables, Laird and Overman (2014)
CASE STUDY
RESEARCH
1. Review of secondary
data
2. Design of primary data
collection instruments
3. Collection of primary
data
4. Analysis of primary data
5. Presentation of results and
discussion
Source: Author’s Construct, (2015)
CENTRAL
INSTITUTIONS
Ministry of Transport
(MoT)
Ministry of Finance and
Economic Planning
Metro. Planning and
Coordinating UnitsDepartment of
Urban Roads
Budget and Rating
Departments
LOCAL INSTITUTIONS
Ghana Private Roads and
Transport Unions
Source: Author’s Construct, (2015)
•Both qualitative and quantitative analytical techniqueswould be approached.
•Quantitative analytical technique in the form ofdescriptive statistics, maps, charts and graphs using GIS,and Microsoft Office Package would be used tocomplement qualitative analysis.
•Qualitative data in the form of self-completingquestionnaires and interviews would be analysed using theStatistical Package for the Social Sciences (SPSS).
Self Completing Questionnaires Would beAdministered to each Institution
The Metropolitan Economic and Policy PlanningOfficers would be granted Recorded In-DepthInterviews
Accra, 89.9
Tamale, 60.1
0
10
20
30
40
50
60
70
80
90
100
0
500000
1000000
1500000
2000000
2500000
Lite
racy
Rat
e
Pop
ulat
ion
Capital Cities
Population and Literacy Rates of Capital Cities in Ghana
Population Literacy rate
Source: Ghana Statistical Service, 2012
“Trotro” Transport Service Station In Accra
References:
• Venables A. J., Laird J. and Overman H, 2014. Transport investment and economic performance: Implications for project appraisal, Available
at: https://www.gov.uk/government/publications/transport-investment-and-economic-performance-tiep-report.
• Ghana Statistical Service, 2012. 2010 Population and Housing Census: Summary Report of Final Results, Accra. Available at:
www.statsghana.gov.gh/docfiles/2010phc/2010_POPULATION_AND_HOUSING_CENSUS_FINAL_RESULTS.pdf.
N
Available from: http://images.search.yahoo.com/yhs/. Accessed: 29/04/2014
Available from: http://images.search.yahoo.com/yhs/. Accessed: 29/04/2014
Available from: http://images.search.yahoo.com/yhs/. Accessed: 29/04/2014
Available from: http://images.search.yahoo.com/yhs/. Accessed: 29/04/2014
The prospects for greening the international shipping industry
Background o The international shipping industry is hugely
important to national economies. o Pollution from global ships is a major blot against
the industry; increasing evidence against the use of diesel engines.
o International shipping volume increased 252% between 1970 and 2012 (UNCTAD, 2013), and is predicted to increase by 300% by 2050 (Lloyds Loading List, 2015).
o More international freight means an increase in external costs.
(supplychainbeyond.com)
Global shipping routes 2011
Methodology Interviews will be conducted with key stakeholders, including: o A senior official of the Port of Ningbo-Zhoushan o A senior employee from Ulstein, a shipbuilders
who manufacture in China. o Members of AECOM’s freight and ports team in
the UK. o Shipping, trade and freight experts from the
University of Nottingham Ningbo, China. o Shipbroker based in London or Hamburg. o Academics from the University of Leeds
Business School. o Activists against pollution from campaign groups
such as Greenpeace or Friends of the Earth. o Employee from Associated British Ports.
Assess and analyse trade and emissions data to predict future trends.
References UNCTAD, 2013. Review of Maritime Transport 2013. Geneva: UNCTAD. Lloyds Loading List, 2015. Pimental, D. Zuniga R. & Morrison, D., 2005. Update on the environmental and economic costs associated with alien species in the United States. Ecological Economics, 52(3), pp.273-288.
(http://en.wikipedia.org/wiki/MSC_Oscar)
Objectives o Identify key strategical developments to reduce long-term
effects associated with shipping. o Rationalise shortcomings within the industry. o Calculate and analyse value of external costs associated with
shipping. o Explore possibilities to internalise such long-term costs. o Apply these findings to information and data obtained
through interviews with stakeholders.
Alexander Ryan – MSc Sustainability (Transport) – [email protected] – 200904177 Supervisor: Dr Anthony Whiteing
Research questions o Are key stakeholders implementing strategies and
technologies that can ‘green’ the industry long-term? o What can be done to internalise external costs? o Would potential strategies dramatically increase the cost of
shipping goods?
Scope o Ships use bunker fuel, which is leftover after oil
has been refined; extremely high sulphur content.
o Reduce the impact of invasive species, which cause $120billion of damage annually in the USA alone (Pimental et al., 2005).
o Destruction of fragile marine habitats e.g. Great Barrier Reef.
o The impact of slow steaming. o Costs attributed to piracy. o Lost cargo loses ship operators and exporting
companies money.
(ordiate.com)
Development in international seaborne trade (Millions of tonnes loaded)
YearOil and
gas
Main
bulks
Other dry
cargo
Total (all
cargoes)
1970 1440 448 717 2605
1980 1871 608 1225 3704
1990 1755 988 1265 4008
2000 2163 1295 2526 5984
2005 2422 1709 2978 7109
2006 2698 1814 3188 7700
2007 2747 1953 3334 8034
2008 2742 2065 3422 8229
2009 2642 2085 3131 7858
2010 2772 2335 3302 8409
2011 2794 2486 3505 8785
2012 2836 2665 3664 9165
(UNCTAD, 2013)
Using new technologies to support sustainable travel behaviour
Objective
Assess how effective new technologies are to
promote the uptake of sustainable travel choices
amongst the student population
at the University of Leeds
Used in step
Method Description 1 2 3 4
Literature review Strategies to promote sustainable travel behaviour and its effectiveness. a a a
Commercial state-of-the-art
Review of solutions offered by commercial companies. a a
Interviews to relevant
stakeholders
Who First Group, WYMetro, University of Leeds Sustainable Development Office, UTravelActive Leeds, Bike Hub and more.
a a a
Why Identify relevant questions they face, success factors and barriers and obtain its critical opinion about the solutions to propose.
Primary data (students)
Focus groups
• Corroborate travel behaviour patterns and barriers. • Recruitment through social networks and mail, with a free weekly bus ticket
reward. a a a
Surveys • Three questions added to the University student travel survey. • Second survey evaluating the proposed solutions. On-line through mail
and personally on campus.
Other data • University student travel survey answers from years 2012 to 2015. • Annual survey performed by WYMetro, including questions about information, as well
as statistics on its website use by sections. a a
Solutions on the scope
Areas of research
A. How to reach
awareness of the
available tools
B. The influence of
information in bus
travel
E. The role of
Smart Payment
F. The decision of
bringing a car to
Leeds
C. First access to
cycling: overcome
barriers for bike
hiring?
D. The influence of
information on
cycling and
walking
Methodology
Will be achieved through four steps:
1. Understand travel behaviour of students
2. Review available products and initiatives
3. Propose improvements to current solutions or complete new solutions
4. Evaluate the proposalsː attractiveness and feasibility
Motivation: Raised as a main concern from industry experts.
Expected results: Best points to include/promote transport information: specific-purpose apps or general
Expected results: Recommendations on the type of tool to prioritize (journey planner, real-time, personalized information) and how to better present this information.
Motivation: Raised as a main concern from industry experts.
Expected results: Assessment of types of smart-payment methods. Proposal on how to better sell an MCard-style ticket to students.
Motivation: 25% do have access to a car in Leeds while less than 7% use it to go to the university.
Expected results: Recommendations on how to discourage bringing a car to Leeds or buying it.
Motivation: Available services of bike hiring in University of Leeds (Bike Hub) and in Leeds city centre (cycling point).
Expected results: Best points to promote a bike hiring service.
Expected results: Recommendations on the type of tool to prioritize (journey planner, real-time, personalized information) and how to better present this information.
Studentː Adrià Ramirez Papell
Source of the images: photographs have been made by the author and screen captures have been obtained from WYMetro website, Facebook and Twitter. Icons of current solutions have been obtained from official webpages or social network accounts.
Journey Planner
Static information Maps, timetables, fares, etc.
Real-time information Bus
Smart payment
Social networks Information and campaigning
Fully automated
vehicle hiring
Supervisor: Jeremy Shires Second reader: Frances Hodgson
INTRODUCTION
MOTIVATION:
o The environmental impact of fossil fuel consumption by the transport sector is a global concern
o Waste cooking oil (WCO) appears to be the most commercial viable biodiesel alternative but impacts are not well understood
WHY WASTE COOKING OIL BIOFUEL?
Like other biofuels, it reduces fossil fuel dependence
BUT compared with ‘unused’ biofuels…
o There is demand/competition for it from other sectors
o Large UK ‘reserve’ so reduced food security issues
o It has a low production cost
o It is estimated to reduce CO2 lifecycle emissions by 90%
DATA (data provided by Dr. Hu Li)
ASSESSING THE SCALE-UP POTENTIAL FOR AN ALTERNATIVE FUEL VEHICLE FLEET
Adrián Ortega Calle (email: [email protected])
RESULTS:
Preliminary analysis indicates that non-intrusive loggers are
typically logging at about 0.25-0.3 Hz (1 measurement every
3-4 seconds randomly)
Blended Mode
Empty Truck
Cold Start
Neat Diesel
Hot Start WCO/DIE
SEL
Loaded Truck
Cold Start
Neat Diesel
Hot Star WCO/DIE
SEL
DATA SETS Vbox
Position
Velocity
PEMS
CO2
NOx
Exhaust Flow
Non-Intrusive Logger
Diesel Consumption
Temperature Flow
Load Number
Supervisors: Karl Ropkins and Hu Li
ECONOMIC ENVIRONMENTAL SOCIETY
• Lower running costs
• Less reliance on fossil fuels
•Reduce global warming (CO2 emissions)
•Potential for lower urban pollution (NO, NO2, HC and PM emissions)
• Improve Air Quality
• Improve quality of life
•Lower health impacts
PROJECT BACKGROUND:
o A commercial UK HGV fleet operator has modified selected
vehicles within their fleet to run on blended WCO/DIESEL o These HGVs use a fuel management system that delivers
a WCO/DIESEL ratio based on engine operating
temperature and load o The fleet operator has been monitoring HGV activity and
some engine data using (non-intrusive) data loggers o The fleet operator together with University of Leeds
have collected higher resolution data, including PEMS (portable emissions measurement systems), in a project led by Dr. Hu Li
BENEFITS
THIS PROJECT :
Will focus on two components of the analysis of data collected by the fleet operator and Dr. Li’s team:
•Hole filling (non-intrusive) data – these loggers collect data intermittently so strategies will be investigated that regularize data and thereby simplify analysis
•Higher level fuel economy analysis – Provisional total journey analysis already been undertaken but the aim is complement this by investigating in-journey performance
DATA ANALYSIS; HOLE FILLING
Method Development: Using higher resolution data (1 Hz PEMS data)
• Make ‘sparse’ subsample by randomly removing measurements, hole fill and compare filled sparse and parent data
• Use this as a test method to compare the performance of different hole filling methods over varying degrees and distributions of sparseness
HDV OPERATING MODES STUDIED
EXAMPLE HGV ROUTE
DATA COLLECTION
Variable engine work dependent (See Results)
Fixed 0.5Hz
Logging Rate
Fixed 0.5Hz OR BETTER
Possible Methods • Single-Value Imputation
• Constant Value Interpolation
• Linear Interpolation
• Non-linear(e.g. Spline) Interpolation
• Multiple Input Model Based Inference
DATA ANALYSIS; MICRO-TRIP ANALYSIS
Chopping the journey data into small portions to analyse and provide detailed information about performance (e.g. on slopes, at junctions, etc.)
Early results from method testing suggest that both linear and
Spline based interpolation methods are reliable hole fitting
options for the purposes of this project
REFERENCES/SOURCES: (1) Map/example vehicle route from SEYED ALI HADAVI, BULAND DIZAYI, HU LI, ALISON TOMLIN. 2015. Emissions from a HGV using Used Cooking Oil as a Fuel under Real World Driving Conditions. SAE Paper 2015-01-0905; (2) plot generated with R, R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/; (3) Figure from https://my.vertica.com/; (4) Plot generated with R, see REF (3), and pem.utils. KARL ROPKINS, AWAT ABDALLA, STEPHEN G. HANLEY (2012). 22nd CRC Real World Emissions Workshop. San Diego, US; (5) Plot generated with R, see REF (3), and lattice, SARKAR, DEEPAYAN (2008) Lattice: Multivariate Data Visualization with R. Springer, New York. ISBN 978-0-387-75968-5; (6) Plot generated with R, see REF (3), lattice, see REF(5), and grey.area, KARL ROPKINS (2015). grey.area package. version 0.1.10.
NEXT STEPS:
Extend the above testing of hole filling methods to a larger test set of data (more vehicles, more different journeys, more variables) to provide ensure the robustness of the selected hole fit methods
To use the ‘best choice’ method to hole fill the HGV data
To use this enhanced data as the basis to more detailed (e.g. micro-trips) analysis of fuel economy data for the HGV fleet
(1)
(3) (2)
(4)
(5)
(6)
High traffic on link A65 and A658,particularly in the peak times, deterioratingtravel time reliability to the airport andpotentially decreasing its level of accessibility.
Airport passenger numbers haveincreased from 1.4 million in 2004 to 4.3 millionin 2011 and the airport management companyhas further plans to increase passengernumbers to 5.1 million in 2016 and 7 million by
2030.The Travel Time from Leeds to LBIA
In PM Peak Times
a) To investigate the impact alternativemeasures intervention such as roadwidening, improving junctioncapacity, implementing bus lane toimprove airport accessibility level inthe term of travel time and cost.
b) To measure the welfare benefit inconsideration of the lower trafficflows in the road networks.
c) To investigate the impact ofalternative measures to the carparking demand at the airport.
1) Collecting Leeds road network and car origins-destinations (O-D) matrix data.
2) Assigning and simulating traffic of Leeds road network.
3) Investigating the flows, generalised cost and travel time in networks accessing to the LBIA
4) Implementing alternative measures to the network.
5) Assigning and simulating the model using SATURN.
6) Analysing the outputs
7) Estimating the welfare benefits using the Rule of a Half principle. As demand in SATURN is fixed the excess trip will be estimated using “pseudo link” analysis
Level of Accessibility (the difference travel time and cost in accessing airport)
Welfare Benefit (Road Users)
New flows and V-C ratio Travel time and cost in accessing airport Demand elasticity
METHODOLOGY
OBJECTIVESBACKGROUND
MODEL OUTPUT
EXPECTED RESULT
RoutesBus Car
Peak Off-Peak Peak Off-Peak
A65-A658-LBIA 43 mins 31 mins 37 mins 25 mins
A660-A658-LBIA - - 26 mins 50 mins
A660-Otley Old Road-LBIA - - 40 mins 22 mins
A65-Horsforth-Scotland Ln-LBIA - - 36 mins 23 mins
The Leeds city region (and its surroundings regions) road networks
Travel demand (O-D matrix) of private car users
Cost of travel and travel time Demand elasticity of car mode
Assignment Methods: Wardrop’s Equilibrium
Frank-Wolfe Algorithm
SCOPE
LBIA
A65 and A658 in Leeds SATURN Network
Below 10 mph
11 – 20
21 – 30
31 – 39
40 – 49
50 – 60
> 60
AM Peak Speed (mph) 7.30 – 9.30
Source: Wharfedale and Airedale Review Development Group, 2011
Source: www.google.co.uk/maps/ 2015
(-): No direct bus access
IMPROVING THE ACCESSIBILITY TO LEEDS BRADFORD INTERNATIONAL AIRPORT
Cost = f(flow)Equilibrium;
Cost route a = Cost route bi.e, 15 + 0.005Va = 10 + 0.002Vb
𝛿 = 𝑇𝑖𝑗𝑟 (𝐶𝑖𝑗𝑟 − 𝐶𝑖𝑗
∗ )𝑖𝑗𝑟
𝑇𝑖𝑗𝑖𝑗 𝐶𝑖𝑗∗
Junctions in A65 Road
Source: Wharfedale and Airedale Review Development Group, 2011A65-B6157
A65-A58 (M) Inner Ring Road
A65-Hawksworth Road
A65-A6120 Outer Ring Road
A65-A658
KirkstallRawdon
Degree of Convergence
Research Modelling Framework in SATURN
Convergence Level
Not Converged
Alternative Measures
(Network Building)
Leeds Road Network (*.UFN file)
Leeds Car O-D Matrix (*.UFM file)
SATALL Leeds (*UFS)Used as
Benchmark
Output Comparison and Performance
Evaluation
Post Analysis (P1X)ConvergedNew Leeds
(*.UFS)Simulation and
Assignment
Leeds Car O-D Matrix
Mitigated Delayin Links
AM Peak:• Rawdon Airdale Works to
Outer Ringroad Junction• Kirkstall Abey to Leeds
Centre
PM Peak:• Leeds city centre to
Kirkstall Lane traffic signals• Horsforth via Outer Ring
Road and Rawdon traffic light
Ahmad Nurdin, [email protected] INSTITUTE FOR TRANSPORT STUDIES
Cost and Efficiency of
Powertrains Oil Price Changes
UK / EU Emissions Policy EURO 6 Standard (2015-2020)
Low Emissions Zones
Subsidies
Factors Effecting Change
Anand Mistry – MSc (Eng) Transport Planning and Engineering Student
Background
Changes to EU legislation regarding emissions, and the increasing
affordability and efficiency of modern powertrains is encouraging a rapid
change to the powertrains used in vehicles in the UK.
(Fleetnews, 2013)
(Ecomento, 2014)
(Mercedez-Benz, 2011)
What will the UK Vehicle Fleet Look Like in 2020?
Literature:
To be gathered:
• Department for Transport and DEFRA publications
• EU and UK government policies and strategies
affecting next 5 years.
Dissertation Supervisor – Dr James Tate
Available Powertrains
Conventional Petrol and Diesel
Petrol and Diesel Hybrids:
• Internal Combustion Electric Vehicle (ICEV)
• Hybrid Electric Vehicle (HEV)
• Plug-in Hybrid Electric Vehicle (PHEV) Range
Extended Electric Vehicles (REEV)
Battery Electric Vehicle (BEV)
Hydrogen Fuel Cell Electric Vehicle (FCEV)
Biofuels
Outcomes
Methodology:
1. Analyse existing data, including: • 24 hour number plate survey in Leeds
• Company car data from SMMT (50% of new sales are company cars)
2. Analyse published trends and literature on:
• Trends of powertrains, vehicle size and weight (from SMMT)
• Impact of economic changes in UK, factors effecting choice of powertrain.
• Examples in other countries.
3. Determine any other required data.
4. Predict different futures based on:
• Oil prices, Government Policy / EU Targets, Different Economic Conditions
Data Sources:
To be retrieved:
• Society of Motor Manufacturers and Traders (SMMT)
Already Gathered:
• Transport for London
• Road Traffic Surveys in Leeds
Objectives To estimate:
Power Trains ● Air Quality Emissions ● Greenhouse
Gas Emissions
(Tate, 2015)
Proportion of Vehicle Fleet by Euro Standard
References
Ecomento, (2014), Image [Online], Accessed 29th April 2015, Available: http://cdn.ecomento.tv/wp-content/uploads/2014/01/VW-Golf-GTE-Plug-in-Hybrid-740x425.jpg
Fleetnews, (2013), Image [Online], Accessed 29th April 2015, Available: (20https://fncdn.blob.core.windows.net/web/1/root/19147_w268.jpg
Mercedez-Benz, (2011), Image, [Online], Accessed 29th April 2015, Available: http://www2.mercedes-benz.co.uk/content/media_library/unitedkingdom/mpc_unitedkingdom/trucks_refresh_2011/more_about_mercedes-benz/environment/euro-vi/how_can_mercedes-benz.object-Single-MEDIA.tmp/euro-help.jpg
Tate, J, (2015), Vehicles Emissions: Measurement and Analysis Lecture
Traffic Survey Leeds, (2015), Query ANPR Results, [Excel Document from Dr James Tate], University of Leeds
BACKGROUND
A. A travel survey is a survey of individual travel behaviour.The result of the survey represent what people do overspace, and how people use transport. One of the optionmethod to analyze the results of a travel survey is byusing a GIS analysis. The advantage of this analysis is ableto transform the survey data into a spatial form.
B. University of Leeds as a destination, attract so manypeople to come from different locations and withdifferent ways to travel. With the number of students at31,906 and 7,517 number of employees (UoL, 2014),there are many possible ways of their journey to get tothe university, according to their personal preferences.
1
WHY GIS ANALYSIS? Can support spatial decision making and capable to integratethe descriptions of locations with the characteristic of thephenomenon that is found in that location.
GIS in land-use suitability analysis aims at identifying the mostappropriate spatial pattern for future land uses according tospecify requirements, preferences, or predictors of someactivity (Hopkins, 1977; Collins et al., 2001).
2
METHODOLOGYA. Spatial Analysis by adding some criteria that are contained in the
travel survey like social-demographic. Technically in ARCGIS, the analysis will do the following functions :
• Measure, spatial query, and classification function• Overlay function• Neighbourhood function• Network function
B. Statistical descriptive analysis to process the data which are difficult to be represented in the spatial form.
4
EXPECTED OUTCOMES
• Spatially represent the analysis of the travel survey.
• The Analysis results can suggest new recommendationsbased on spatial, such as a new pedestrian path, location ofparking provision, cycle roads, or a new public transportservices.
7
Source:1. http://conistonbillsgarage.co.uk/
2. http://immediateentourage.com/3. http://www.mevaseret.org/
4. http://skalgubbar.se/Map based : google maps
1
2
3
4
OBJECTIVES and SCOPE
• To identify the distribution of origin place ofemployees of University of Leeds.
• To identify the dominant factors that influencepeople in making their way to the university.
• To bring the existing of public transportservices
• To compare and analyze the current travelconditions of existing provision network asfuture plan by the university and the citycouncil.
3
DATA6
PRIMARY DATA
• in the form of survey results was supplied by the ITS.
• The number of Respondents totaled about 2,500 employees.
SPATIAL DATA
• map of West Yorkshire in which already includes transport infrastructures, such as road networks, bus stations, parking lots, cycle roads, and pedestrian.
DOCUMENTS
• development plan documents by the university and city council.
REFERENCES
Collins, M.G., Steiner, F.R., Rushman, M.J. (2001). Land-use suitability analysis in the United States: historical development and promising technological achievements. Environmental Management 28 (5), 611–621.
Hopkins, L.. (1977). Methods for generating land suitability maps: a comparative evaluation. Journal for American Institute of Planners 34 (1), 19–29.
University of Leeds. Facts and Figures Section http://www.leeds.ac.uk/info/20014/about/234/facts_and_figures
8
GIS ANALYSIS SAMPLE5
The potential use of Stone Mastic Asphalt (sma) surface course on the Kuwait highway network
Aims 1. To establish an efficient procedure that will remedy the Kuwait
highway pavements problems. 2. To provide a set of methods and suggestion that would be practical in
Kuwait.
Objectives 1. To establish a comparison between two types of asphalt 2. Determine what kind of chemical additives can be used
in the asphalt 3. To design the new road structure 4. To present the results in logical and cost efficient way
Methodology 1. On this study a compressive analysis of existing
literature and design techniques will be used to develop a solution that could be applied on the Kuwait highway network
2. Data will be gathered from previous works on the subject to develop a literature piece of work to compare the use of stone mastic asphalt and the commonly used hot mixed asphalt and determine what are the risks that accompany its usage
3. To analyse the main problems being faced by conducting site visits to the most damaged areas and roads so the source of the problem can be found using knowledge gained from learning the aspects of pavements and roads from lecture notes and available literature.
Background Kuwait is a country located in the Middle east, It currently has over 4 million people living in it and because of its geographical location Kuwait’s weather can be very severe ranging from very hot summers (over 50 degrees) to very cold winters (-5 degrees) which raises an issue, Kuwait has nine main highways constantly being used by all people and all sorts of vehicles from HGV’s to small cars which results in extreme pavement damage on those highways due to the constant heavy vehicle usage on them. During the summer the high temperatures causes extreme movement on the asphalt surface resulting in what is known as rutting and in winter the cold weather causes constant cracks on the road surface and weak spots. With this research a solution might be found in the use of stone mastic asphalt instead of hot mixed asphalt because of its weather and load resistant properties.
Benefits of stone mastic asphalt: • Better resistant to pavement deformation • High wearing resistance • Less cracking • Coarse surface structure • Good macro roughness • Good long term behaviour • High skid resistance • A high amount of coarse aggregate • High binder content • Stabilizing additives
Stone mastic asphalt Stone mastic asphalt was first used and made in Germany in the 1960s on heavily traffic roads and still being used since then because that specific mix provide the wanted protection on heavily trafficked roads. Resulting in a mix strong like the Gussaphalt mix but can paved transported like asphalt concrete.
Expected findings • Stone mastic asphalt would be eligible use in Kuwait. • A large amount of high quality coarse aggregate and additives provider
would be needed for the construction of the road. • Temperature of the asphalt has to be controlled to avoid any cold
spots occurring on the pavement • Usage would be on part of the road being used by HGV’s to reduce the
cost of construction
Paving and distributions: • Compacting should be done as soon as possible and as close as
possible to the pavers. • At least two rollers are required for each lane that is to be paved • the roller compaction should be done using a tandem or a three
wheel roller with operating weight not less than 9 tons
References
Student: Abdulhadi Kazem Supervisor: Eng. David Rockliff Course: Transportation planning and engineering
What Can Travel History Interviews Tell Us About Mobility Characteristics?
1. INTRODUCTION
How do people move every day?In Great Britain
1952
42 27 3
11 17 0.1
2013
5 83 1
1 9 1.1
1 x per month : 86.3%
1 x per week : 77.3%
3 x per week : 54.7%
5 x per week : 43.7%
Proportion of residents who walk at least 10 minutes continuousEngland, 2012/13
In percentage
In percentage
Source : Transportation Statistics Great Britain (2014)National Travel Survey (2013)
2. RESEARCH BACKGROUND
• Conventional transport modelling has been around for the last five decades or soand is still popular among transport planners
• While it may has solved transport demands according to planners and decisionmakers, how about the ‘users’ perspective on the transport system especially in UK?
• EPSRC sponsor a research project conducted by ITS University of Leeds, School ofCivil Engineering University of Birmingham and ESRC CRESC University of Manchestercalled the STEP CHANGE (Sustainable Transport Evidence and modelling Paradigms:Cohort Household Analysis to support New Goals in Engineering Design) project.
• The project aims to understand how people behaviour change over time and todevelop a new modelling paradigms that recognize the complexity of people travel’spractices rather than the current emphasize on travel costs.
• STEP CHANGE conducted surveys and interviews to 240 households around Leedsand Manchester and observe the changes and continuities in their transportbehaviour related to their background, circumstances, life histories and everydaylives.
• This dissertation project aim to understand people mobility by analysing data thatwas conducted from the STEP CHANGE project. Mobility itself is increasingly popularwithin transport studies as sustainable urban environment is often established basedon how the people travel.
3. RESEARCH OBJECTIVES
How do people perceive their mobility all along?
What factors affect them to prefer a specific modes
of transportation?
Are there any different perspective within different generational cohort (Baby Boomers, gen X, gen Y)?
Can we develop new transport modelling paradigms based on our understanding of people mobility?
4. LITERATURE REVIEW
Mobility
Objects able or
capable of movement
Mob (Disorder Group of
Movement)
Vertical Hierarchy
of Positions
Migration
Macro MobilityWalking
Cycling
Driving
Etc.
Generic Mobility
• The proliferation of places, technologies and gates enhance the mobilitiesof some while reinforcing the immobilities of others.
• Time spent traveling is not necessarily unproductive that people alwayswish to minimize. Movement often involves an embodied experience ofthe material and sociable modes of dwelling-in-motion.
• Activities conducted while traveling including the ‘anti-activity’ of relaxing,thinking, shifting gears and the pleasure of travelling itself, including thesensation of speed, of movement through and exposure to theenvironment, the beauty of a route and so on.
John Urry in Mobilities (2007)
5. METHODOLOGY
Research Objective
Literature Review
Data Collection
STEP CHANGE
Data
Data Management and Analysis
NVivo
Findings and Results
Conclusion
By : Adhi Bukhari Hernowo Putra (M.Sc.) Transport Planning Supervisor : Dr. David Milne
0
200
400
600
800
1,000
1,200
0-16 17-20 21-29 30-39 40-49 50-59 60-69 70+
Tri
ps
pe
r P
ers
on
/Ye
ar
Walk Bicycle Car / van driver
Car / van passenger Other private transport1 Local and non-local buses
Rail2 Taxi / minicab Other public transport3
In Depth Interviews: Mobility pattern
o Transformation of individual mobility over time Significant event in life View toward other modes of transportation
• Identify the general pattern of households mobility in Leeds and Manchester
• Identify people perspective on different type of mobility and possibly perspectives from different generational cohort
• Identify the main problem in Leeds and Manchester transportation system that may represent UK in general
Context CRPs experience extra demand increases ●
Volunteers add value to rail industry ● The recent Northern Invitation to Tender (ITT)
requires bidders to support and develop CRPs ●
Growing rail demand works toward achieving sustainability goals ●
CRPs have a
4:1 BCR for
investment(2)
Objectives Understand and document the actions taken
by CRPs ● Establish links between actions and demand
on specific lines ● Understand public perception of CRPs ●
Develop best practice for CRPs ● Inform the rail industry of potential for CRPs
to increase demand on local lines ● Place CRPs within the policy framework ●
Community Rail Partnerships (CRPs) and
Impacts on Passenger Rail Demand
Student: Alexander Heard
Supervisor: Dr Mark Wardman
What actions do CRPs take? ● What impact do these actions have on demand?
What is ‘best practice’ for CRPs?
References (1)Transport Regeneration Ltd, 2008. The Value of Community Rail
Partnerships. Bury St Edmunds: Transport Regeneration Ltd (2)Transport Regeneration Ltd, 2015. The Value of Community Rail
Volunteering. Bury St Edmunds: Transport Regeneration Ltd
What are Community
Rail Partnerships? Over 50 CRPs in the UK ● Specified by the Department for Transport -CRPs bring together:
• Infrastructure operator (Network Rail)
• Train service provider (TOCs) • Volunteers
CRP lines:
+2.8% yearly
demand
increases
above other
lines(1)
Analysis & Discussion Link specific actions and their perceptions across CRPs
to trends in demand to understand their effect
Develop a portfolio of best practice actions most effective in increasing demand
5-10 CRP’s in the North, covering a range of
population density and demand trends, mindful of local demand influences.
Methodology
2
Demand data
Plotting ORR station usage
data to examine trends in demand for CRP lines vs. non-CRP lines
Linear regression
LENNON ticket
sales data – excel analysis
1
Information
from CRPs
Compiling CRP actions from
newsletters and articles
Consulting the
CRPs to determine the
actions that they take and their
goals
3
Passenger
survey data
Site visits
Market research questionnaires
Perception of
changes delivered by CRPs
Data
“Community rail partnerships
are a bridge between the railway and local communities. (…) Some
partnerships have been instrumental in achieving spectacular increases in use of rail” – ACORP Website
What do they do?
maintain station facilities ● advertise train services ● engage with communities ● organise events ●
develop intermodal options ● aim to increase demand ●
TRAN5911 Poster presentation, May 2015; images Mid-Cheshire CRP
Use ARCADY to determine Capacity and delays at the existing roundabout.
Use LINSIG to signalise roundabout and to tabulate the delays.
Replacing the existing roundabout by designing Continuous flow intersection .
Use VISSIM to carry out the micro simulation of the three options to calculate the
idling emissions based on the data obtained from transport models.
Hypotheses testing for the for emissions, driver perception and efficiency of CFI in
reference to a normal roundabout and signalised roundabout.
MULTI-CRITERIA ANALYSIS OF CONTINUOUS FLOW INTERSECTIONBy Amir Farooq(MSc. Transport Planning and Engineering) ¦ Supervisor: Dr. Haibo Chen ¦ 2nd Reader: Dr. Yvonne Barnard
Also referred as displaced right turn intersection, CFI is a displaced crossover junction which takes the right turning movement away from the junction to increase efficiency at the Intersection.
Data Collection And Methodology
• Works on the principle of reducing the conflict points at the central node bycreating a new crossover for right turning movements. The relocated right turningmovement creates a new 2 stage intersection.
• It was introduced in Mexico in early 2000’s as an alternate to grade and at-grade intersections.• CFI’s have been observed to achieve a reduction of 30%- 70% in travel time and intersection delay.• Problems have been faced with respect to driver expectancy and comfort, and a negative public
perception.• Other problems with Continuous flow intersections is with respect its complex signal operations,
longer pedestrian crossings, corner business impacts, and a potential for more user delays in lighttraffic conditions.
More about CFI
Need for S
tudy?
0
5
10
15
20
25
30
35
40
45
50
Delays(AM Peakin 0's Sec.)
Speeds( AMPeak in Kmph)
Delays(PM Peakin 0's Sec.)
Speeds( PMPeak in Kmph)
Roundabout High Capacity Signals Continuous Flow Intersection
Performance Statistics for Paulsgrove Roundabout Roundabout redesign options (Source: JCT Report on CFI)
How
?Data Co
llection
Data An
alysis
Multi‐crite
ria Ana
lysis
Research Questions
As a case study for this analysis, A660/A6120 Weetwood roundabout is used to compare performance of CFI to a normal roundabout, signalised roundabout.
Primary sources of data – Parameters for the existing roundabout, Questionnaires
for driver perception of for CFI’s, Simulator Studies?
Secondary sources of data-
Classified turn based traffic count from 2002 AIMSUN model of the Headingley
corridor ,developed by Halcrow(for Leeds Super tram project).
Extract results for emissions data from well established transport models.
A multi criteria analysis of continuous flow intersection for the Weetwood junction to be
carried out based on the Indicators obtained from the Data analysis of emissions data , driver
behaviour and efficiency variables. It would involve weighing and scoring of each indicator to
make choices and analysis.
Can reduction in conflict points by CFI help improve
efficiency at intersections? If yes, is it significantly
improved?
Does CFI produce reduction in the environmental
impacts of traffic at intersection?
Will it cause driver confusion due to its un-conventional
design? How significant is the driver confusion?
Intersection time distribution*
7%12%
37%
44%
5%9%
17%
69%Through Green
Amber
Red
Right Green
Four arm signalised Intersection 2 Arm CFI
Criteria for Performance
Driver BehaviourEnvironmentalEfficiency
Suitable Solution
Literature Review Micro simulation
Roundabout assessment Signalised roundabout
Questionnaires Multi‐Criteria Analysis
Week 21‐Week 24
Week 23 ‐Week28
Week 12‐Week 20
Week 34 ‐Week 39
Week29 ‐Week33
Week 40‐Week 43
Congestion Driver acceptanceDriver adaptationCO2,NOXFuel ConsumptionEffect on Pedestrians
Capacity
1. Background:
• Reliability is a key factor for rail passengers.
• There is a need for an intra-modal reliability
metric for the rail industry.
• This will enable passengers to see the likelihood
of their train arriving at their desired destination
on time.
2. Literature Review:
• The only publically available reliability
information comes from Public Performance
Measure but this is not helpful for passengers.
• There is currently no information for rail
passengers about the reliability of an intra-modal
journey or even a specific journey.
• Reliability is a key factor influencing demand and
passengers have to factor in reliability when
planning journeys (de Jong and Bliemer, 2015).
5. Scope:
• This project will focus on 5 main origin-
destination paths as summarised in table 1.
• An airport was chosen as the destination as
they have the largest reliability elasticities
(Wardman and Batley, 2014).
4. Objectives and aim of this report:
• Objective 1: To develop a reliability metric for
intra-modal trips to Manchester Airport.
• Objective 2: To present the data findings in a
format which is best for rail passengers.
Origin Option Location of first
change
Time for
connection (mins)
Location of second
change
Time for
connection (mins)
Regularity Average journey time
(mins)
Brighouse 1 Huddersfield 10 Manchester Piccadilly 15 Hourly 90-95
2 Huddersfield 25 Hourly 95
3 Manchester Victoria 6 Salford Crescent 8 Hourly 115
4 Mirfield 11 Huddersfield 5 Infrequent 110
Ilkley 1 Leeds 13 Twice an hour 120-150
Mossley
(Manchester)
1 Stalybridge 5 Manchester Piccadilly 13 Hourly 56
Knottingley 1 Leeds 28 Hourly 146
2 Wakefield Kirkgate 5 Meadowhall 7 Hourly 150-160
Cottingley 1 Huddersfield 5 Hourly 105
2 Dewsbury 5 Manchester Piccadilly 6 Evening Peak 95
Measuring reliability for intra-modal rail journeys:
A journey planner approach – Andrew Carson
Data
collection
• Data collected on arrival and departure times from
train services in table 1.
Data
analysis
• Once the data has been collected the number of
intra-modal journeys that arrive at their destination
on time will be calculated.
Data
presentation
• The data will be presented in a similar style to
Table 1. with an additional column of the reliability
of the service.
Data
evaluation
• Once the data has been presented for the first
time it will be shown to members of the public in a
focus group(s).
• As a result of this focus group the presentation
will be developed for a final output which is best
for passengers.
Train at Manchester Airport (Mike Peel, 2009, sourced Wikipedia, 2015)
6. Methodology:
Table 1: Typology of journeys to be studied
Key References:
de Jong, G. and Bliemer, M. (2015) ‘On including travel time reliability of road traffic in appraisal’, Transportation Research Part A: Policy and Practice, 73, pp.80-95
Marsden, G., Shires, J.D. and Wardman, M. (2014) Integrated information for integrated transport – Final report for transport systems catapult’, Institute for Transport Studies, Leeds
Peel, M. (2009) A British Rail Class 323 train at Manchester Airport railway station, sourced; Wikipedia (2015) Manchester Airport Railway Station, [online], available at
http://commons.wikimedia.org/wiki/File:Manchester_Airport_Railway_Station_1.jpg, licensed under CC-BY-SA 4.0 Wardman, M. and Batley, R. (2014) ‘Travel time reliability: a review of late time valuations, elasticities and demand impacts in passenger rail market in Great Britain’, Transportation, 41, pp. 1041-1069
3. Key Aim: To provide simple and clear
information on intra-modal journey
reliability, for rail passengers.
