Mel Course 2003 Ecorys

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Table of contents

1 Introduction 6

2 Trade and traffic demand 72.1 Introduction 7

2.1.1 Levels of decision-making 72.1.2 Study phases 10

2.2 Trade and traffic generation 112.2.1 Passenger transport 11

2.2.2 Freight transport 12

2.2.3 Maritime applications 12

2.2.4 Homogeneous cargoes 13

2.2.5 Heterogeneous cargoes 14

2.2.6 Some practical mathematical functions 14

2.2.7 An example: the relation container throughput and economic activity 16

2.3 Trade and traffic distribution 18

2.3.1 General 18

2.3.2 Distribution models 19

2.3.3 Origin constrained 20

2.3.4 Destination constrained 20

2.3.5 Double constrained models 20

2.3.6 The impact of travel costs 22

2.3.7 Linear programming (LP) 242.3.8 RAS method 24

2.3.9 Generalised costs 25

2.3.10Maritime applications 26

2.4 Assessment of shipping type, ship size and route 272.4.1 Introduction 27

2.4.2 Physical appearance 27

2.4.3 Container penetration 27

2.4.4 Assessment type of shipping operation 27

2.4.5 Assessment of ship size and vessel traffic 28

2.4.6 Assessment of shipping route (or choice of ports) 28

3 Demand choice models 303.1 Generalised costs 30

3.1.1 Passenger transport 30

3.1.2 Freight transport 313.1.3 Costs and the choice of an alternative 31

3.2 Some examples of transport choice positions 33

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3.2.1 Urban and inter-urban transport applications 33

3.2.2 Demand choice applications involving maritime transport 34

3.3 The logit model 35

3.3.1 Model description 353.3.2 The assessment of coefficients 37

3.4 Baltic Sea feeder transport versus land transport 39

3.4.1 Background 39

3.4.2 Transport costs 40

3.4.3 Quality of service aspects 41

3.4.4 Generalised costs: costs and quality of service aspects together 42

3.4.5 Specification logit model 42

3.4.6 Results of alternative choice models 42

3.5 Market share of the container port of Rotterdam 43

3.5.1 Introduction 433.5.2 Model specification and estimation of coefficients 44

3.5.3 Application of the model 48

3.6 Modal split short sea shipping versus road transport 503.6.1 Interactions between users and producers of services 50

3.6.2 Structure of short-sea modal split model 51

3.6.3 The modal split function applied 53

3.6.4 Results 54

3.7 Estimation parameters of a liner service choice function 55

3.7.1 The choice of liner shipping services 55

3.7.2 The stated preference survey 57

3.7.3 Ordinal versus metric responses 573.7.4 Example of an interview and estimated utilities 59

3.7.5 Result of the pilot survey 59

3.7.6 Assessment of utility coefficients with ACA method 63

4 Transport supply 654.1 Capacity of infrastructure 65

4.1.1 User and producer costs 654.1.2 Road capacity and congestion 66

4.2 Port capacity assessment 69

4.2.1 Port capacity as an economic trade-off 69

4.2.2 A port capacity formula 714.2.3 Benchmarks as applied in Gujarat State in India 72

4.2.4 Benchmarks as mentioned by Frankel 73

4.2.5 Benchmarks for modern container terminals mentioned by Drewry 73

4.2.6 Use of tables based on queuing theory 75

4.3 The Maasvlakte-2 project 76

4.4 Puerto America project 78

4.4.1 Queuing theory 78

4.4.2 Components of the simulation model 79

4.4.3 Schematisation of the wet infrastructure of the harbour 81

4.4.4 Description of the simulation process 82

4.4.5 Input data 834.4.6 Description of the output 85


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4.4.7 Simulation runs 86

4.4.8 Results of the first set of runs 86

4.4.9 Conclusion 89

5 Costs of transport 925.1 Introduction 92

5.2 Operator costs and time of transport services 92

5.2.1 Costs and time of ro-ro services 92

5.2.2 Costs and time of road transport 96

5.3 Issues on socio-economic costs 98

5.3.1 The Unite project 98

5.3.2 Internalising external costs 98

5.3.3 Cost categories 100

5.3.4 Relationship between marginal, average and total costs 1015.4 Socio-economic costs by category 102

5.4.1 Infrastructure costs 102

5.4.2 Supplier operating costs 1035.4.3 User costs and benefits 104

5.4.4 Accident costs 105

5.4.5 Environmental costs 105

6 Evaluation of infrastructure projects 1166.1 Cost benefit analysis 116

6.1.1 Role of cost benefit analysis 116

6.1.2 Producer and consumer surplus 1176.1.3 Economic costs and benefits 119

6.2 The Puerto America project in Venezuela 120

6.2.1 Project description 120

6.2.2 Economic benefits 121

6.2.3 The optimum port draft alternative 122

6.2.4 Importance of benefits by category 122

6.2.5 Sensitivity analysis 1236.2.6 Incidence of benefits 124

6.2.7 The Maasvlakte-2 Project of the Port of Rotterdam 125

6.3 Small scale example: removal of shoal in the port of Abidjan - 128

6.3.1 Background 1286.3.2 Project definition 130

6.3.3 Trade and traffic developments 130

6.3.4 Port charges 132

6.3.5 The types of economic benefits 132

6.3.6 Time costs savings 133

6.3.7 Increase in traffic safety 133

6.3.8 Land reclamation 134

6.4 Financial evaluation and port finance 136

6.4.1 Introduction 136

6.4.2 Some lingo 137

6.4.3 Finance and port management models 1376.4.4 Factors influencing type of private finance 140

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6.4.5 Key elements of financial analysis 141

6.5 Practical applications on pricing of infrastructure 142

6.5.1 General 142

6.5.2 Roads 1426.5.3 Waterways 143

6.5.4 Ports (1) The ATENCO project 148

6.5.5 Ports (2): Some examples 149

7 Miscellaneous issues 1527.1 Liner shipping networks 152

7.1.1 Introduction 152

7.1.2 The position of the ports in the United Arab Emirates (UAE) 155

7.1.3 Relative advantage of the various container ports 157

7.1.4 Sample data PortScan South Africa 1617.1.5 Sample data PortScan South East Asia 163

7.2 Costing transhipment port options 166

7.2.1 Introduction 1667.2.2 The cost model 166

7.2.3 The case of the Arabian Sea Area 168

7.2.4 The case Southeast Asia 173

Annex 1 176


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2 Trade and traffic demand

2.1 Introduction

Transport demand concerns the movements of passengers and goods. The movements of

passengers are governed by consumer behaviour and the movement of goods by producer

behaviour. The latter can find its theoretical foundation on economic theories such as those

based on profit maximisation. Despite the difference in the economic foundation, the models

applied to quantify movements of passengers and goods show great similarity. This chapter

concentrates on the similarities and starts with the phases in transport decision-making by

consumers and producers and finally focuses on maritime transport and ports.

Transport demand studies can be distinguished by their focus, such as by:

1. Mode of transport: rail, road, inland waterway, sea and air;

2. Type of activity: point-to point movements and interposal transfers and storage such as in

seaports and airports; and

3. Geographic scope: urban, inter-urban, regional, international and inter-continental.

The demand models discussed produce outputs needed for the comparison with the supply of

infrastructure such as railways, road, inland waterways and uni-modal or multi-modal transfer

points such as logistic centres, inland waterway ports, seaports and airports.

2.1.1 Levels of decision-making

With respect to transport demand for goods three levels of decision-making can be

distinguished:

1. The level of locations (trade generation)concerns: Location of industries, number of goods produced.

Total of imported and exported goods per firm or region.

2. The level of relations (trade distribution)concerns:

The choice of trade partners.

The assessment of trade relations between regions.

3. The level of operations concerns:

choice of transport mode, scheduled versus non-scheduled, choice of transport route and

vehicle size.

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The analyses discussed here focus on the flows of goods or flows of passengers moving between

regions. The decision-makers are the collective of individual firms in the case of transport of

goods and the individuals in the case of passengers. The behaviour of the firms can be based on

economic theory of profit maximisation and those of passenger based on consumer behaviour.Despite this dissimilarity the quantitative models applied most often look rather much the same.

Trade generation, i.e. the assessment of incoming and outgoing cargoes per region, concerns

long-term decision-making. Trade distribution, the choice of trading partners by importers and

exporters generally concerns medium to long-term decision-making. Modal split, choice of

transport route and choice of size of vehicles all are operational issues and often of a shorter-

term nature. See figure 2.1.

Figure 2.1 Levels of decision-making

Source: Tavasszy, Modelling European Freight Transport Flows, PhD Thesis, 1996

Based on these levels of decision-making the classic four-stage transport demand model hasbeen derived1that is applied to the movements of goods and passengers corresponding with thisapproach is given in figure 2.2. The four basic phases are:

(1) trip generation, i.e. passenger movements or in the case of goods cargo flows ormovements;

(2) trip distribution;(3) modal split; and(4) assignment.

For a traveller the four stages of decision-making are:1. Yes/no to make trip;2. Yes/no to select a certain origin or destination;

1Handbook of transport modelling, Edited by Hensher, D. and Button J., Chapter 2 History of Demand Modelling by Bates. J., Volume 1,

Pergamon, 2000.

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3. To choose a certain means of transport; and4. To choose a certain route.

In the first phase of traffic generation and attraction the numbers of trips or volume of cargoflows coming in and going out is assessed on the basis of the characteristics of the regions

concerned. This aspect is worked out in Section 2.2.

In the second phase, the distribution phase, the outgoing trips or cargo flows are allocated to or

distributed over the various destinations resulting in an origin destination table or matrix (OD-

table). This aspect is worked out in Chapter 2.3.

The third phase involves the choice of mode. The various trips or flows of the OD-table are

allocated to the different modes of transport:

in case of passengers: car, bicycle, public transport means(bus, rail, air plane);

in case of freight: truck, train, river barge, sea ship, and airplane.

An important distinction often coinciding with the type of vehicle is between scheduled (train,bus, plane, passenger ferry for passenger transport and train, plane, trailer ferry, ro-ro ship andcontainer ship for freight transport) and non-scheduled transport (passenger cars, bicycles, shipsinvolved in tramp shipping).

In the last phase, the assignment phase, the various flows by mode are transformed into vehicle

movements, which are subsequently assigned to the corresponding networks such as:

passenger car movements to a road network.

passengers to public transport to rail or bus line services.

freight movements by truck to a road network; freight movements by rail to a rail network;

freight movements by barge transport to an inland waterway network;

freight movements in container over sea to liner shipping services.

It should be noted that for maritime transport the choice of the size and type of vehicle is very

important for planning purposes. In case of bulk shipping for instance the sizes of ships can

range from a small bulk carrier of a few hundred dwt to a large one of more than 400,000 tons

dwt, such as the Berge Stahl. For port planning for instance it is one of the major issues

determining the role and dimensions of a port.

So far it is assumed that the transport decisions are taken in the above sequence. Sometimes the

decisions are taken in an integral way, i.e. the decision of choice of destination, mode of

transport and transport route are taken together. The stages may be applied in an iterative way,

i.e. with steps back to the previous stage in order to gain more accuracy of information. See

arrows between blocks in figure 2.2.

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Figure 2.2 The classic four-stage transport model

Source: Ortuzar, J. de D., Willumsen, J.G., Modelling Transport, John Wiley & Sons, England, 1990

2.1.2 Study phases

In the preparatory phase (see the 4 blocks in the top of figure 2.2) the area of influence is

defined and is divided in traffic zones or regions and a corresponding network of infrastructure

or transport services. Passenger flows are divided according to the traffic motive of the

travellers, such as:

home to work;

home to school;

office-to-office; other purposes such as trips for leisure, social or other purposes.

In a similar way freight flows are divided according to type of commodity and its physical

appearance. See Sections 2.2.3 and 2.2.4.

The evaluation phase (see block at the bottom of figure 2.2) may concern the financial or

economic evaluation of investments in infrastructure (rail, road and ports), the performance of

transport services (bus lines, railway lines or shipping lines), the identification of bottlenecks in

for instance a rail or road network or, more general, the assessment of the impact of policy

measures.

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Studies for urban transport aim to identify bottlenecks in urban transport systems and to

investigate the impact of changes in the availability of infrastructure. These models are

becoming fairly standard practice and a great amount of commercial software has beendeveloped and is available on the market such as EMME/2, SATURN and QRS. As passenger

cars generally dominate urban road networks, at least in the peak hours, passenger transport

models receive most attention.

Studies for inter-urban or corridor transport aim to investigate the impact of changes in the

availability of infrastructure or of measures to influence the modal split. At present in the

European Union many studies are going on to investigate the impact of measures to trigger a

shift from road to public transport such as rail, inland waterway or maritime transport. For long

distance or international transport the same types of models are used.

For corridor studies modal split is an important issue. With respect to passengers it concerns

road versus rail, normal train versus high-speed train, airplane versus high-speed train. With

respect to goods it concerns trucks versus trains, trucks versus maritime transport, maritime

transport as roll-on-roll-off (ro-ro) and lift-on-lift-off (lo-lo) and so on. The studies all

concentrate issues of an operational level.

2.2 Trade and traffic generation

2.2.1 Passenger transport

Traffic generation consists of a push effect, the generation/production of passengers trips or

cargo movements and of a pull effect, the attraction of passengers and cargo movements. For

passenger traffic concerning home-work trips, the production side coincides with the decision

to choose a place for living and the corresponding attraction side corresponds with the decision

to choose a place to work. As home-work traffic is a dominant part of traffic, in peak-hours in

particular, the prediction of this type of trips is an important element with urban traffic studies.

Once a division of the study area in traffic zones has been made the number of working and

living places per zone has to be established. For home-school traffic and work-to-work (or

business) traffic similar assessments have to be made. Thus, important activities for the study

preparation are zoning of the area and classification of trips according to traffic motives.

The following factors are mentioned to determine trip production by person or group of persons

in a geographical area (traffic zone):

Income

Car ownership

Household structure

Family size

Value of land

Residential density

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Accessibility

Characteristics of land use are used for the explanation of trip attraction, factors such as:

Office space

Retail space Employment level

Quantitative relationships are investigated between the numbers of arriving and departingpassengers per zone as a function of the above factors. The types of models are often linearmodels of which the parameters are estimated by regression analysis. These analyses are done

per traffic motive separately. For details and quantitative examples see Ortuzar2, Chapter 4.

2.2.2 Freight transport

With freight transport generation/production concerns the assessment of flows of incoming andoutgoing goods. The decision by a firm to decide to produce a certain level of goods leads to

outgoing flows of products and simultaneously a level of incoming goods such as raw materials

and semi-manufactured goods. By dividing goods transported into recognisable entities one can

establish a relation between decisions of production and levels of incoming and outgoing flows

of goods. In the clearest case this concerns well identifiable flows of goods such as incoming

crude oil and outgoing petroleum products for production decisions concerning a refinery. For

less clearly identifiable aggregates such as iron and steel products for a number of steel based

industries located in a certain region the relationship is looser. For aggregates such as building

materials or even more aggregated groups such as containerised goods the relation becomes

very loose.

To understand the development of incoming and outgoing goods, one can investigate production

history and production plans of the industries concerned, a refinery and the related petro-

chemical industry, an iron and steel industry, a construction industry in case of well identifiable

goods. In case of less well identifiable aggregates such as consumer goods, capital goods,

containerised goods, the relation with the specific industry is lost and one has to find relations

between the totals of incoming and outgoing goods and indicators of economic activity such as

Gross Regional Domestic Product (for a region) or even population.

2.2.3 Maritime applications

For maritime freight transport the four stage model can be elaborated in the following project

steps, as given in figure 2.3. The preparatory and evaluation phases and possible feedbacks

between the consecutive phases are omitted. The maritime applications concern worldwide

studies such as those by Drewry Consultants for the maritime transport of major, minor and neo-

bulk goods and regional studies on for instance short sea shipping and port studies. It should be

2Modelling transport, Ortuzar, J, de D. and Willumsen, L.G., John Wiley & Sons, England, 1990

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noted that in practical applications, however, some of the steps might not be noted, as they are

trivial. This may concerns the phases 2 and 3 in particular.

Figure 2.3 Phases of port trade and traffic forecast

The forecast of port trade and traffic is part of the activities to assess the need for measures to be

taken such as expanding and/or improving the capacity of a port, to reorganise or restructure a

port or to update a masterplan of a port. The improvement of the port itself may have an impact

on the outcome of the trade and traffic forecast. This means that there need to be feedbacks

between the consecutive phases of the forecasting process.

Before proceeding it is useful to make a broad distinction between homogeneous and

heterogeneous commodity groupings.

2.2.4 Homogeneous cargoes

For homogeneous types of cargo such as crude oil, petroleum products, iron ore, coal andcereals, the so-called major bulk commodities, it is often possible to do an analysis of surplusesand deficits for the hinterland regions. These analyses are based on industrial plans for such

distribution

type of shipping size of shipment choice of route

demand of ships

by type and size

surplus -deficitanalysis

demand

&

consumption

supply

&

production

Phase 1

Phase 2

Phase 3

Phase 4

Phase 5

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industries (oil refineries, steel industries, chemical industries etc.), which have a longconstruction lead-time and which are known a long time in advance. In such a case the porteconomist has to make a judgement on the nature and likely timing of such programs and to

make based on that his own assessment. This also applies to the so-called minor bulkcommodities such as non-ferro metals, timber and cereals and neo-bulk commodities. For theseproducts there are also publications on worldwide production, consumption and marketassessments. In these cases the port economist only has to collect the various reports and makehis own adaptation to the specific situation.

For homogenous cargoes the incoming and outgoing cargoes per region are equal to thedifference of production and consumption of the goods. Production is based on existing andplanned expansion of production capacities: crude oil production, petroleum productsproduction etc. Consumption of goods can be for the use by industries, such as iron ore by thesteel industry, chemical feedstock by the chemical industry and fertilisers by agriculture.Consumption can also be for final consumers, such as grain for human food and can then be

related to the size of the population and the increase in consumption per capita.

Drewry shipping consultants is one of the leading consultants selling reports on the situation onthe various shipping and port markets. (Seewww.drewry.co.uk)

2.2.5 Heterogeneous cargoes

For heterogeneous cargo groupings such as general cargo, unitised cargoes or wheeled cargoes,the approach is different. These cargoes generally concern an aggregate of all types of consumergoods, capital goods and semi-manufactured products. Often these groupings result bysubtracting from a total all well identifiable major and minor bulk goods and by subsequently

re-grouping the remainder in manageable groups such as those of an agricultural nature,construction materials and food products. Theoretically, it would be possible to conduct asurplus and deficit analysis for each sub-group separately. In practice, however, this would oftenbe a waste of energy considering the detail of information and therefore the effort needed.

The total volume of incoming and outgoing unitisable cargoes can be taken as one group or as anumber of groups distinguished by economic sector, which can be forecast each separately or asone group. The development of incoming cargoes of commodity group construction materialsin the past can for instance be linked to the development of the production of the constructionsector or to the level of investments.

2.2.6 Some practical mathematical functions

A mathematical relationship has to be chosen and the parameters of the function concerned needto be estimated by for instance regression analysis. The mathematical relationships often chosenare linear or log-linear and can be applied for the total or for cargoes by sector.

Growth functions

The following growth functions are often used:(2.1) xt= x0(1+g)

t

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where:xt : transport flow in year tx0 : base year

g : annual growth ratet : time in years

Sometimes instead is used:(2.2) xt= ae

gtwhere :a : constante : base of natural logarithmg : growth rate

The proposed mathematical relationships between incoming and outgoing cargo flows andindicators of economic activity such as GDP, could be for instance:

Relationships between flows of goods and explanatory variables

(2.3) x = 0GDP1

where:x = transport flow

0 = constant factor

1 = elasticity

The coefficient1 is referred to as elasticity and can be expressed as:

(2.4) 1= (x/x)/(GDP/GDP)

For the use of elasticities in maritime economics, see Evans and Marlow

3

A convenient property of it is that, if GDP increases with 1%, the total transport flow will

increase with 1x 1% = 1%. This relation holds for infinitesimal small values of the growth

rate. For small changes it is often used as follows:

(2.5) 1 = {(xt+1- xt)/ xt}/{(GDPt+1- GDPt)/ GDPt}

In practice the value of1 is often estimated on the basis of a time-series of values of x and GDP

in the following three ways by:

a. Applying regression analysis according to equation (2.3);

b. Calculating the average value according to equation (2.5) for a number of yearto-year

changes; and

c. Calculating the annual growth rates of x and GDP over the whole period and then simplydividing the growth rate of the former by the growth rate of the latter.

An example of the application of this function is given in Section 2.2.7.

A linear relationship to predict flows of goods

(2.6) x = 0 + 1x GDP

3Evans, J.J. and Marlow, P.B., Quantitative Methods in Maritime Economics, Second Edtion, Fairplay Publications Ltd, UK, 1990

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The impact of the factors is little for the flows of the so-called major and minor liquid and dry bulk

goods and the handling of these goods in ports. For neo-bulk commodities such as iron and steel

products, timber products, temperature controlled goods and agro-bulks there are some interesting

developments with respect to supply chain management. In these concepts processing and valueadded services are to be located in well-chosen seaports. Ro-ro shipping is also playing an

increasing role for these goods.

For general cargo flows, however, the impact is great, leading to a strong increase in trade, to

economies of scale in shipping and ports and to an overall increase in the importance of quality of

service. The container liner-shipping network is becoming more fine-meshed, ships on all services

are becoming bigger and, for certain segments, also faster. The companies involved have to adapt

themselves continuously to the changing world. This applies to the shipping industry, which has

seen a pattern of mergers (P&O Nedlloyd, Mearsk/Sealand) and to the port management industry,

which has seen an internationalisation of the industry with companies playing in all parts of the

world (Hutchison Whampoa, Singapore Port Authority and P&O Ports).

A new development is that geographically there is a relative increase of trades within world

regions, such within the Far East, within the Indian Ocean and within the Mediterranean.

Some quantitative justification of the increase in trade intensity can be derived from the worldwide

development of container trades. The factors determining the increase of container throughput

concern the development of:

1. economic activity in general;

2. logistics and technology such as container penetration;3. trade intensity of the various sectors of the economy;

4. shipping systems such as multi-porting and hub and spoke systems; and

5. port competition leading to shifts in port hinterland.

Growth of GDP and container throughput

A statistical relation was measured between GDP and container throughput. The figures used were

established in such a way that only the factors one, two and three play a role. The container

throughput figures do not include transhipment, so that the strong increase due to double handling

is eliminated. See point 4. The size of the regions is so big that the impact of port competition

between regions is negligible. The main point remaining concerns developments in trade intensity.

The ratio of the annual growth of container throughput and of GDP (calculated according to

approach c of equation 2.5), to be referred in short as the elasticity, shows for a number of

world regions values ranging from 1.7 to 3.7. The start from a very low basis of containerisation

explains the high value for some regions. If the start of the period is put at 1985 instead of 1980,

the extremes are somewhat lower. The high values, however, remain. See table 2.1.

The interesting point is that similar values apply to both industrialised regions such as Europe,

North America and the Far East (including the then called tiger economies) and to developing

regions. One could expect that the industrialised regions of North Europe and North America, with

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their high degree of container penetration, considered to have come close to saturation, would

show much lower ratios.

Table 2.1 Elasticity of container throughput versus GDP for world coastline regions.

Coastal region\ period 1980-1996 1985-1996

North Europe 2.7 2.4

South Europe 3.7 3.3

Middle East 2.6 2.1

South Asia a N/A. 2.1

SE Asia 2.5 2.4

Far East 2.6 2.7

North America 2.4 2.4

Oceania 1.7 1.8

Africa 3.1 2.4

World 2.6 2.8

[Source: Internal study MERC/NEI based on container throughput data presented in Short SeaContainer Markets, September 1997, Drewry Shipping Consultants Ltd]

Note: throughput figures do not include transhipment

The interesting thing of this analysis is that the values of the elasticities generally are

considerably higher than one, both for regions with predominantly developed and developing

countries. An explanation for the developed regions could be the increase of trade intensity as

mentioned above. For the developing regions the reason could be the later start of

containerisation.

2.3 Trade and traffic distribution

2.3.1 General

After insight is gained on the number of departing and arriving trips (in the case of passengers)and on the amount of incoming or outgoing cargo flows, the next step is to distribute these tripsand flows over the combinations of origins and destinations, i.e. the assessment oforigin/destination pairs (OD-pairs).

The passenger or cargo flows are allocated to or distributed over the various destinations. This

can be presented in an origin destination table or matrix, which can take a form as given in table

2.2. Assume there are n regions numbered from 1 to N. The rows for instance can represent the

cargo flows originating in one of the regions numbered from 1 to N. The columns can represent

the cargo flows with a destination in one of the regions numbered from 1 to N. It should be

noted that the set of origin regions and destination regions do not necessarily have to coincide.

Thus, a cargo flow from region 3 to region 2 is given the notation T32. In a more general way.

The cargo flow from region i to region j is given the notation Tij. All outgoing flows from

regions i add up to Oi. In a similar way, all incoming flows for region j add up to Dj.

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In formula for all flows going out of region i:

(2.8) =

=

Nj

j

Tij1

= Oi

and for all flows coming into region j:

(2.9) =

=

Ni

i

Tij1

= Dj

The totals Oi and Djare the result of the generation and attraction phase and the cargo flows Tij

are the subject of the distribution phase. The regions i and j do not necessarily have to coincide

and may be different. In the formulation given in table 2.2 there are assumed to be N origin

regions and M destination regions. This for instance applies to the table of iron ore flows from

producing regions to consuming regions.

With respect to the relationship between production/attraction and distribution three possible

options exist:

1. both Oi or Dj are given As the result of the production/attraction phase;

2. only Oi is given; or

3. only Djis given.

Specification (1) is called doubly constrained and the other two singly constrained.

Table 2.2 General form of the Origin Destination table

Origins destinations

=

=

Mj

j

Tij1

1 2 j .. M

1 T11 T12 T1 T1j T1.. T1M O1

2 T21 T22 T2 T2j T2.. T2M O2

3 T31 T32 T3 T3j T3.. T3M O3

T1 T2 T Tj T.. TM O

I Ti1 Ti2 Ti Tij Ti.. TiM OI

T1 T2 T Tj T.. TM O

N Tn1 Tn2 Tn T2nj Tn.. TnM ON

=

=

Ni

i

Tij1

D1 D2 D Dj D.. DM ==

==

MjNi

ji

Tij,

1,1

2.3.2 Distribution models

Distribution models can be distinguished in singly and doubly constrained models, depending

on whether per region the level of the outgoing flows or of incoming flows is fixed (singly

constrained). If both are fixed, the model is doubly constrained.

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2.3.3 Origin constrained

A practical example of an origin constrained distribution model applied for passenger transport

is the WOLOCAS5model used to predict trip distributions for new residential areas. The tripsTijhave to meet condition (2.8) and are divided over the set of destination regions according to

the equation:

(2.10) Tij= aiOiXjFij

where:ai : a balancing factor;Xj: a factor indicating the potential of region or zone j;Fij: a factor indicating the accessibility of zone j from zone i.

The attraction abilities of the destination regions vary per trip purpose and can be for instance:- the employment level of zone j, in case of home-work travel;- the number of shops in case of in case of shopping travel; and- the number of inhabitants in case of travel for social purposes.These models are used to assess the number of trips generated by new residential areas.See Ortuzar p. 136.

2.3.4 Destination constrained

A destination-constrained model can be:(2.11) Tij= bjDjXiFijwhere:bj : a balancing factor; and

Xi: a factor indicating the potential of region or zone i.

The attraction abilities of the origin regions can be:

the number of houses of zone i, in case of home-work travel;

the number of houses in case of in case of home-shop travel; and

the number of inhabitants in case of travel for social purposes.

These models are used to assess the number of trips generated by new shopping malls, industrial

centres and so on.

2.3.5 Double constrained models

Several approaches exist to assess the flows of goods or passengers trips between regions or

zones given the set of origin and destination totals:

5WOLOCAS, acronym for WOonLOCcatie scanner, a model developed by INRO/TNO to assess the amount of outgoing trips by

destination for new residential areas. 1990

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1) gravity types of models with a deterrence or cost function: this approach is very common in

trip forecasting. Most software used for urban planning includes gravity types of models. It

is less commonly used with freight transport;

2) LP models assuming an optimising behaviour is commonly used in freight transport in casethere is good reason to assume that optimisation takes place. The LP model can also be seen

as a special extreme case of the gravity model (1); and

3) RAS techniques6to reproduce a basic OD structure by proportionally adapting row and

column totals to the given set of totals. The technique is also called bi-proportionate

fitting or the Furness technique named after Furness.

The distribution of trips by trip length category expressed in travel time is given in table 2.4.

After an initial increase the number of trips is decreasing strongly. For bicycle, car and train the

ship of the curves is different. See figure 2.5.

Figure 2.4 Number of trips as a function of trip length expressed travel time (minutes)

6Bates J, 2000, page 29; Ortuzar & Willumsen 1990, p 136

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Figure 2.5 Number of trips by mode as a function of trip length in km

2.3.6 The impact of travel costs

Various approaches are applied. The general distribution model for a double constrained model

is:

(2.12) Tij= aibjOiDjFijWhere:

Tij = tons of cargo moved from i to j;

Oi , Dj = incoming and outgoing flows for region i and j;

ai,bj = balancing factors corresponding with region i and j, these factors indicate the

importance of the area of origin i and destination j for a particular flow from i to j;

Fij = indicating the accessibility of one region compared to the other

(2.13) Fij= f(Cij)

f(Cij) = the deterrence or cost function: a function of the generalised transport costs per ton

from region i to j.Cij = generalised transport costs per ton from region i to j.

The cost or deterrence function makes that the further region i is away from region j and as a

result, the higher are the transport costs and travel time, the smaller the flow of transport from i

to j. The most popular forms of the deterrence function are:

(2.13a) f (Cij) = (Cij)-n a negative power function

(2.13b) f (Cij) = exp(-Cij) a negative exponential form

(2.13c) f (Cij) = (Cij)-nexp(-Cij) a combined function

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where:

, n = parameters of the deterrence or cost function

Examples of the shapes of the various curves are given in figure 2.6.

The larger the value of the coefficient, the greater the (negative) impact of costs on the volume

of traffic flows. In case the coefficient takes a value of zero, the impact of costs i nil. In case the

coefficient takes a very high value, it can be proven mathematically that the solution takes a

value where total transportation cost are minimised according to a solution of minimisation of

costs according to linear programming.

For more details on distribution modelling see Ortuzar7. Although the applications presented

concern passenger transport, one can easily replace passenger trips by cargo flow

movements.

Figure 2.6 Frequency of trips as a function of trip length expressed trip costs

The starting point of the forecast concerns the values of Oi , Dj for the future. One can assess the

flows Tijby applying equation (2.12). Calculation of the values produces a result where the

values do not automatically add up to the constraints Oi , Dj and have to be adapted by a method

which involves successive proportional corrections by rows and than by columns to satisfy the

constraints. The algorithm stops when the corrections are small enough, i.e. when the constraints

7On the pages 145 to 146 a method is given as to how to assess the parameter and the balancing factors a i,bj of the model.

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are met within a predefined level of accuracy. By mathematicians this method is referred to as

bi-proportional fitting, by transport planners as the Furness method and by economists as the

RAS method. For an example of the method see Ortuzar Section 4.5.

Application of the model leads to a situation where there are cargo or passengers flows between

all region pairs. This is acceptable for passenger flows and flows of heterogeneous cargo

groupings in a system with a fine-meshed network where there are flows between all regions.

2.3.7 Linear programming (LP)

In some cases market conditions are such that there is a tendency towards minimisation of

transport costs. In the case of re-positioning of empty containers the shipping lines aim at

minimising transport costs and a simple model could look like:

(2.14) Minimise: C = CijTijMjNi

ji

==

==

,

1,1

under the condition that

(2.15) =

=

Mj

j

Tij1

= Oi

and

(2.16) =

=

Ni

i

Tij1

= Dj

The total costs C are equal or less than the total costs according to any of the other above-

mentioned solutions.

2.3.8 RAS method

In case there is a great difference between the cargo flows of the base year and the cargo flows

as assessed according to either the distribution model or the LP model, one can use the cargo

flows of the base year as a starting point. In this way the basic pattern of flows, for instance the

pattern as experienced in the past, is reproduced for the future. This for instance applies quite

well for the forecast of crude oil flows and petroleum product flows between world regions. The

basic matrix shows a considerable number of empty cells and application of a gravity model

would lead to too much small flows and thereby to an under-estimation of the large flows.

This technique is for instance applied to a market study for traffic passing the Suez Canal, where

it was important to have good forecasts of the oil flows between the Arabian Gulf area and

North America and the Caribbean, which are prone to switches between the Suez Canal and

Cape of Good Hope route. Forecasts of crude oil movements were made given forecasts of

energy and oil production and consumption of world regions. The RAS method was used to

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assess the oil flows applying crude oil flow patterns of the past as starting value. This approach

gave better results than the application of a distribution model with a deterrence function or LP.

In case there are important determining factors other than (generalised) costs, one can forinstance use the OD-pattern (the structure of Tij), as observed during the past as a starting point.

This approach is less common in land transport applications, as possible improvements in the

values of Cijare disregarded. Such improvements are less relevant in planning of sea transport,

where improvements of the transport network do not apply: at sea there is practically no

congestion. In planning of land transport, these improvements themselves are the subject of

analysis.

The technique to predict the values of traffic flows Tij for a future target year with new values of

a given structure as experienced in the past based or on a pattern calculated with new values of

Oi and Dj and/or new values of Cijis discussed below.

The initial summed values of Tijwill not be equal to the sum values of Oi andDj. By repeatedly

adapting the values of Tijto these totals, the adapted values appear to converge. This process of

repeated proportional adaptation over columns and rows until conversion is reached, is referred

to as the Furness- method (see Ortuzar and Willumsen, p. 134-135).

2.3.9 Generalised costs

The costs as used in the various types of deterrence or distance functions concern generalised

costs8

, that means it concerns all costs as experienced by the user and supplier of transportservices. In its simplest form generalised cost is a linear function of travel costs and time, the

latter being converted in to money units by means of the so-called value of travel time

savings. It can be defined wider by including any variable that is likely to impact on travel or

transport decisions in the wider sense. It concerns all costs to produce a transport service as they

are put into a price for a transport service and the costs of the users of transport services. See

also Sections 3.3, 4.1 and 5.4.3.

8Bates 2000, p. 12

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2.3.10 Maritime applications

In a number of cases the maritime planner needs to have detailed knowledge of cargo flows

between certain regions and the interactions, which exist between such flows. This applies toplanning with both shipping and ports.

Examples with shipping are:

the repositioning of empty containers by a liner shipping operator, where the operational

department is planning the repositioning of empty container in such a way that it minimises

costs;

Another example concerns the Suez Canal Authority which wants to make assessments of

the worldwide flows of crude oil passing the canal and therefore studies the crude oil

shipping patterns at a global level;

planning of inter-island shipping in archipelagic nations such as Indonesia, Greece and

Philippines; and

a shipping company interested in a particular flow between two regions.

Examples with ports are: The prediction of the flows to or from a certain region. The investor in a crude oil or coal

export port in, say Venezuela, wants to know the level oil sold to the US against the sales toWest Europe. This also applies to coal exports of Venezuela. Sales to the US go by smallerships and those to West Europe go by bigger ones.

In a strongly simplified form: the assessment of the ratio of aggregates of origins anddestinations, such as short sea shipping against deep-sea shipping within the total cargoes.

A special case: parts of the hinterland of a port may have the option to import or export over

sea from one group of trading partners and over land from the other group. In such a caseassessments have to be made on the future share of transport over sea.

For the prediction of the share of these large aggregates one can also extrapolate the shares

themselves, using market functions such as those of (2.7a) and (2.7b).

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The types of ships and ship operations include:

Tankers: parcel tankers, product tankers and crude oil tankers.

Dry bulk carriers.

Combined carriers. Liner ships: container ships, ro-ro ships, multi-purpose ships, semi-container ships, and

tween-deckers.

Specialised ships: cement carriers, heavy lift ships, LPG tankers, and vehicle carriers.

For port planning purposes with container transport the shift from tween-deckers and multi-

purpose vessels to fully cellular containerships is an important matter and further also the shift

from liner ships to for instance specialised ships such as in the case of timber shipments.

2.4.5 Assessment of ship size and vessel traffic

With some port studies the size of ships is the main issue of the concern. For instance, a crude

oil port can accommodate only Panamax vessels and the port authority wants to know whether it

is feasible to dredge the ports entrance in order to be able to accommodate VLCCs and reap

the benefits of economies of ship size. Several investment alternatives with respect to the

maximum draft are investigated in order to assess the optimum one. In this case the port

economist investigates to what extent the ports trading partners face limitations in draft and

makes his assessment as to what extent larger ships can be accommodated. This type of studies

applies to import and export facilities of many liquid and dry bulk terminals and to major bulk

facilities in particular.

With some port studies the sizes of ships are of lesser concern, as there is no limitation in size at

present. The port planner has to verify whether this may change in the future due to changes in

shipping technology. At present the largest bulk carriers employed in the coal trades are up to

about 150,000 dwt and many ports are dimensioned on these ships. A port authority intending to

invest in a new terminal has to critically assess the probability that larger ships will come at a

future date. At the moment this applies in particular to container shipping. The issue is very

relevant because of the continuing increase in ship size and the discussions in the maritime press

about the design of much larger ships such as the Malaccamax of 18,000 TEU.

For the planning of berthing space, dimensioning of fairways and vessel traffic systems an

assessment needs to be made on the number of vessel movements by ship type and size class.The assessment of the development of the size of ships can be based on developments in

worldwide shipping and the place of the port within these developments. Often this is done by

simple extrapolation of the average size of ships calling.

2.4.6 Assessment of shipping route (or choice of ports)

Seaports are part of a worldwide network that is becoming increasingly more fine-meshed, for

containers in particular. This means that for a certain origin destination pair the number of

routings (or multi-modal paths along which a container can be routed) is increasing. The choice

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of a certain container routing therefore implies the choice of port. This in fact means that a

container port has to be considered as a part of a network and that one has to know about the

structure of such a network. The choice of container port is dealt with in Section 3.5. Some

issues with the choice of transhipment port are discussed in Section 7.2.

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3 Demand choice models

3.1 Generalised costs

3.1.1 Passenger transport

If travellers are considering to choose between alternatives (a trip by car or a trip by public

transport; a trip by car via a toll road or a trip by car via a free road), they consider there

monetary costs, the travel time and sometimes distinguish between the various travel time

components such as in-vehicle time, waiting time and time walking. See for instance Ortuzar

19909. In formula

(3.1) G = a0D + a1C + a2T1+a3 T2 + a4T3 + a5H

where:G : generalised costC: door-to-door traffic costs: traffic fare and so onT1: in-vehicle traffic time

T2: waiting time at stopsT3: interchange time (if any)H: terminal associated cost (parking cost, if any)D: preference or penalty for a mode

In travel demand modelling for passenger transport there is a huge wealth of models with

respect to:

their use in the process of travel decision-making: (the decision yes/no to make a trip,

yes/no to go to a certain destination, yes/no to choose a certain mode and yes/no chose a

certain route and also the simultaneous integration of some of these decisions);

mathematical form (Logit, Probit);

the inclusion of all these decisions in commercially available software such as EMME/2,Saturn and QRS; and

the widespread use in urban transport planning.

The application of such models in freight transport is less developed and even much less in

planning with respect to seaports and maritime transport. The internationalisation of the ports

industry, however, is changing this picture.

9Ortuzar 1990, p. 131

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3.1.2 Freight transport

For the users of transport services the costs they are considering when making a choice are morethan the costs of door-to-door transport that they have to pay to the transport service providers.

Also of importance are the costs related to transit time, the time span between two consecutive

sailings (sometimes referred to as headtime or inter-arrival time, which time is equal to the

365/F(F= service frequency expressed as the number of sailings per year) and reliability of

service. The latter can be expressed in time as the average amount of time a sailing is off-

schedule.

The costs the users experience internally relate to stock carrying and interest costs. Often all

these aspects together are referred to as quality of services aspects and they play an important

role, when users have to choose between alternatives and their sum is referred to as generalised

cost. The costs to be paid to the providers of transport costs are referred to as the provider or

producer costs and the costs as experienced internally by the users of transport services as the

user costs.

In its simplest form generalised cost is a linear function of all these aspects where travel costs

and time, time being converted into money units by means of the so-called value of travel

time ratio, are taken together. These aspects can be defined wider by including any variable

that is likely to have an impact on travel or transport choice decisions.

(3.2) G = a1C + a2T + a3IAT +a4OST

where:

G : generalised costs

C: out-of-pocket door-to-door costs

T: door-to-door transit time

IAT: time between two consecutive departures

OST: average amount of time a shipment is off-schedule, i.e. too early or too late.

The coefficients a1, a2and a3represent indicators of the various types of time or costs and the

theoretical foundation of the time differences can be based on cost minimisation regarding for

instance stock carrying behaviour.

3.1.3 Costs and the choice of an alternative

If a traveller or a shipper is choosing between two alternatives, he can choose the cheapest

alternative, i.e. the alternative with the least generalised cost. In a deterministic approach this is

a choice of either alternative A or alternative B. If Gi> Gj, then alternative j is chosen;

otherwise, alternative i is chosen. The choice is all-or nothing (AON-choice), see thick line in

figure 3.1, which concerns the choice of a land routing versus a feeder-line routing. If the

generalised cost of feeder minus land routing is positive the land routing is chosen, otherwise

the feeder routing.

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Figure 3.1 Market share as a function of generalised cost difference of land and feeder routing

0

0,2

0,4

0,6

0,8

1

-150 -100 -50 0 50 100

cost di fference feeder - land routi ng

Share of feeder rou ting (%)

3.2 Some examples of transport choice positions

3.2.1 Urban and inter-urban transport applications

Application of transport demand models for urban transport planning is rather common and is

applied generally for all medium and big size cities. Information on the commercial software is

readily available on the Internet, such as the packages EMME/2 Transportation Planning

Software, Quick Response System (QRS) and Saturn. These software packages offer the user

the possibility to forecast traffic movements via an urban road network and a public transport

network and have many possibilities with respect to model specifications such as the application

of the classic four-stage model as discussed in Section 2.1 and the modern variants where trip

distribution and modal split are addressed simultaneously and applications where the choice of

time of departure is included and various types of assignment models. Logit models are a

normal feature of the choice options in the models.

These models are intended to produce a situation of equilibrium between the demand side and

the supply side of urban transport, where the supply concerns the structure and dimensions of

the road or public transport network. The demand side corresponds with the models as described

in Chapter 2 and the supply side concerns the speed-flow functions to be discussed briefly in

Section 4.1. To achieve equilibrium the models apply iterative procedures with the various

stages of the transport model.

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The SATURN12traffic model, was used to simulate the situation for the city of Brussels and

distinguished 184 zones covering the whole of Belgium of which 101 zones cover the capital

city, 66 its periphery and 17 the rest of Belgium. The road network was described by 5305 links

and 2032 intersections. Other applications of the model were for Edinburgh with 25 spatialzones, which is a rather low number; for Helsinki with 145 zones and for Salzburg with 369

zones. Given the resulting enormous amount of information needed to load these models and

similar amounts of outputs, it may be clear that it will be a problem to evaluate the outcome and

this in fact can only be done with graphic presentations.

For long distance transport planning such as with respect to intra-European transport the

European multi-model network model VACLAV13is popular. With inter-urban transport

corridors such as Paris Brussels, Paris Munich, Cologne Milan and Duisburg- Mannheim

transport of goods will play a more important role, while homework travel is being replaced by

travel for business purposes. Also other modes such as rail, air and inland waterway transport

will play a role.

It can be concluded that the models contain interesting features also for application in a

maritime setting. The graphical presentation of results is tremendous. However, the focus is

generally very different from maritime applications, where the impact of capacity constraints in

the network is generally very limited. The application of the software for maritime planning

purposes is often not very useful and sometimes looks like cracking a nut with a sledgehammer.

3.2.2 Demand choice applications involving maritime transport

Users of transport services face many choices when arranging their shipments. Some examples:

A. An importer in Poland of electronics from the Far East can have his goods shipped bymainliner to a North Sea port, further shipped by feeder vessel to the Polish port of Gdyniaand then further by road to his premises. An alternative would be to have it carried directlyfrom the North Sea port over land. The shipment via Gdynia would be cheaper, but wouldtake more time. This trade-off issue is dealt with in Section 3.4.

B. An exporter of machinery in the Ruhr area in Germany to the US can have his exportsshipped via each of the North Sea ports in the Hamburg - Antwerp range by road, rail andbarge and further shipped by mainliner to the US. A similar choice applies to Dutchimporters of electronics from Japan. These choice issues are dealt with in Section 3.5

C. A German importer of Portuguese agricultural products most probably has his goods carriedby lorry. The alternative of having it carried by sea either by trailer on a ro-ro vessel or incontainer on a containership is at present in practice not yet available at a reasonable degree.This modal split issue is dealt with in Section 3.6.

D. The selection of a liner shipping service by shippers and receiver on the Europe Far Easttrade route is subject of analysis in Section 3.7.

12UNITE Case Studies 7

Eand 7F.

13UNITE Case Study 7C and 7D

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Many importers and exporters in practice may not do the choice actively, but have it done byintermediaries such as their forwarding agents or logistic service providers and most often donot bother how it is arranged. They only want to know whether it is arranged according to their

wishes and prevailing standards. If it does not, they will look for other options.

3.3 The logit model

3.3.1 Model description

The logit model as applied here corresponds with the model in the difference form as discussed

above. The probability of choosing routing r (a combination of a shipping line, a port of transfer

and a mode of transport) from all possible routings can be expressed as:

(3.3)e

e

=1..M)=r|r=(mPU

M=r

=1r

U

mr

m

where:

Pm: probability of choosing routing m from all possible ones r = 1..M

Um: the 'utility' attached to route by;

m: routing index;

The probability function Pmapplies to all flows of containers carried from region A to region B

and vice versa. For the sake of simplicity the index of the regions is omitted.

The utility functionThe value, which shippers and receivers located in a certain region attach to routing m, ismeasured in the 'utility14' This utility is generally expressed as a (linear) combination of allaspects or attributes being of importance for the choice between alternative routings. This utilitycan be expressed as:

(3.4) R+F+T+C+D=U m4mm3mm2mm1mm0mm

where:Dm: dummy variable indicating whether shippers/receivers have a reference for

routing m;Cm: shipping costs for routing m inclusive of freight rate, handling charges, land

transport costs etc.

Tm: transit time for routing m;Fm: frequency of service of routing m;Rm: reliability of service of routing m;

0m, 1m, 2m, 3m, 4mare the coefficients of the utility functionThe explanatory variables Cm, Tm, Fm, and Rmare referred to as attributes.

14.Sometimes referred to as 'generalised costs'. It should be mentioned that also other than linear functions can be usedsuch as multiplicative ones.

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Figure 3.1 The share of a routing as a function of the difference in attributes

0

0,2

0,4

0,6

0,8

1

-150 -100 -50 0 50 100 150

Difference in attribute

Share (%)

Figure 3.2 Ratio of coefficients of utility function

25

27,5

30

32,5

35

37,5

40

42,5

45

750 1000 1250 1500 1750 2000

coefficient 1

coefficient2

3.3.2 The assessment of coefficients

Stated and revealed preferences

Logit models are used to make predictions of demand choice. Ideally the coefficients of the

utility function of the logit model should be estimated for a situation that is as close as possible

to this choice position and deals with the choices actually made. Statistical data used with such

revealed choices are referred to as revealed preference (RP) data. The alternative concerns

information based on stated preferences, i.e. preferences decision-makers state to have, when

being asked about hypothetical choices.

Stated preferences (SP) deal with choices made by individual decision-makers such as a

traveller in case of passenger transport or a firm in case of freight transport. Passengers fill in

questionnaires or are being interviewed about which mode of transport they have chosen in a

particular situation. The choices made can then be tested by estimating with statistical

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techniques the parameters of the model on the basis of the values of the attributes of the model.

In the practice of passenger transport this often concerns the choice of means of transport

between home and work. In case of freight transport representatives of the firms are interviewed

about the modal split of the shipments of intermediate goods such raw materials and semi-manufactured goods and of the shipments of their products. This of course only concern the

shipments they decide about and not the shipments decided upon by their trading partners.

It may be clear that there will be a difference between the situation of the actual choice and of

the hypothetical choice. Especially in freight transport such differences may exist as the process

of decision-making generally involves more people within a firm and at more levels: strategic

versus operational. Despite this weakness, the great advantage of SP data is that they generally

are cheap to collect.

With SP interviews the decision-makers are faced with a number of hypothetical situations and

asked which situation they consider to be better than the other. They may be given a number of

choices and than just asked to rank one option against the other and put the different situations

in order of increasing preference (Ranking of preferences). They also may state to what extent

they find option A better than option B according to a scale varying from 0 to 1 (Scaling of

preferences). Software has been developed to generate choice positions and statistically

estimate the coefficients of the Logit model. See example in Section 3.7.

Aggregate and disaggregate dataFor revealed preference data a distinction is made between aggregate and disaggregate data.

Data based on information from the individual decision-makers, such as travellers or

representatives of firms are referred to as disaggregate data. Data based on aggregates ofindividuals such averages of regions or zones are referred to as aggregate data. Through

aggregation of the individual decision-makers the shares of certain choices can be calculated.

With regression analysis or other statistical techniques the relation between these market shares

and the average values of the explanatory variables, the attributes of the utility function, can be

estimated.

It may be clear that in the case of aggregate data information is lost through aggregation and

averaging of the explanatory variables. The advantage, however, is that for the application of the

parameters of the model for prediction purposes on the same situation no aggregation is needed.

The greater accuracy of values estimated with disaggregate data, may be lost when the valuesare used for prediction purposes and have to be aggregated and averaged.

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Figure 3.3 Options to estimate parameters of a logit model

3.4 Baltic Sea feeder transport versus land transport

3.4.1 Background

Polish seaports face competition from other, foreign ports and from each other. Ocean-going

container traffic can be routed to and from Poland directly via Polish ports, indirectly via North Sea

ports to Polish ports, or via North Sea ports such as Hamburg and Rotterdam overland to inlandorigins/destinations in Poland.

At present no major liner shipping services operating on the Transatlantic and Far East trade routes

are calling at Polish ports. Cargoes for Poland are handled at North Sea ports and shipped either

over land (mostly Via Hamburg and therefore referred to as the Hamburg routing) or by feeder ship

to Poland (predominantly via Gdynia and therefore referred to as the Gdynia routing). Direct calls

at Polish ports are at present only made on intra-European services and by services on secondary

container routes.

The importance of the Hamburg routing differs for the various regions of Poland and the types ofcargo/customers. For the regions close to the Baltic Container Terminal in Gdynia the Gdynia

routing is important. The importance decreases for regions further to the Southwest such as for the

industrial centres of Warsaw, Poznan and Lodz and practically vanishes for the industrial areas of

Silezia. Note that the values of the example presented hereafter concern the situation in the

beginning of the 1990ies.

Estimation of

parameters

Revealed preferences Stated preferences

Aggregated

data

Disaggregated

dataScaling of options Ranking of options

Estimation of

parameters

Revealed preferences Stated preferences

Aggregated

data

Disaggregated

dataScaling of options Ranking of options

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3.1 Map of Poland

3.4.2 Transport costs

For the various regions of Poland the situation is different and the least cost routing of containers

to/from ocean-going destinations/origins such as North America and the Far East can be assessed as

follows.

The additional cost (additional to the ocean sea freight which is the same for each alternative) of a

TEU carried to or from the Baltic Container Terminal in Gdynia and transhipped via a North Sea

port is US$ 300-400 for the TransAtlantic route. Similar slightly higher additional costs are

mentioned for the Europe-Far East trade route. These rates include the cost of double handling inthe North Sea port, amounting up to US$ 200, leaving US$ 100-200.

The costs of road haulage from a North Sea port, Hamburg is taken as an example, amount to a

fixed amount per TEU of US$ 37.5 and a variable amount of one US$ per TEU/kilometre. Road

haulage costs without border crossing in Poland are 20% lower and contain a fixed amount of US$

30 per TEU and a variable one of US$ 0.80 per TEU-kilometre. The various cost data are given in

table 3.1. For shipments to the Warsaw region this results in a cost advantage for the Gdynia

routing of US$ 209. See column (2) in table 3.2.

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In terms of costs it appears that there are clear cost differences for the Gdynia routing ranging from

a cost advantage of US$ 409 for the regions close to Gdynia in the North East of Poland to a cost

disadvantages of US$ 254 for the Silesia regions in the southwest. If cost only would play a role

and if shippers and/or receivers all would select the cheapest route, the number of containers routedvia the port of Gdynia would add up to 55,700 TEU on the total of 100,100 TEU. See column with

5 in table 3.2. This selection is not a realistic option, as will be argued hereafter.

Table 3.1 Cost and transit time differences for Polish regions

Region distance (km) costs in USD per TEU Time in days

Gdynia Hamburg Gdynia Hamburg Diff. Gdynia Hamburg Differ.

Warsaw 293 836 664 874 -209 6,4 1,7 4,7

Lodz 405 753 754 791 -37 6,6 1,5 5,0

Krakow 669 853 965 891 75 6,9 1,7 5,2

Wroclaw 579 602 893 640 254 6,8 1,3 5,5

Poznan 408 531 756 569 188 6,6 1,2 5,3

Gdansk 20 739 446 777 -331 6,0 1,5 4,5

Szczecin 340 404 702 442 261 6,5 1,1 5,4

Torun 250 786 630 824 -194 6,3 1,6 4,8

Katowice 598 783 908 821 88 6,8 1,6 5,2

Radom 395 920 746 958 -212 6,5 1,8 4,8

Lublin 454 999 793 1037 -243 6,6 1,9 4,7

Bialystok 487 1191 820 1229 -409 6,7 2,2 4,5

3.4.3 Quality of service aspects

Surveys on quality of service aspects such as transit time, service frequency, service reliability and

aspects dealing with the provision of EDI services and responsiveness to customer needs are of

great importance. Transit time is the most important one. The time span from arrival of the ocean

carrier in Hamburg to the subsequent arrival of the feeder ship in Gdynia is about six days, allowing

for transhipment time in Hamburg, time spent at sea and time lost with matching of main line and

feeder line ship. In 1993 there were three feeder services with a service frequency of one trip per

week. Additional time for loading or unloading in Gdynia is about one day and further transport to

Warsaw comes at 0.4 days. The total time span since between arrivals of ocean carrier in Hamburg

and arrival at the customer's premises in Warsaw comes at 6.4 days. See time data in table 3.1.

The Hamburg routing is much quicker. Road haulage between Hamburg and Warsaw comes at 1.7

days inclusive a 12 hours waiting time loss at the Polish-German border. The resulting time

advantage for the Hamburg routing comes at 4.7 days. For the various Polish regions the

differences range from 4.5 to 5.5 days. See column (3) in table 3.2.

If time only would play a role and if shippers and/or receivers all would select the cheapest route,

the number of containers routed via the port of Gdynia would add up to 0.0 TEU on the total of

100,100 TEU. See column 6 in table 3.2. This all or nothing selection is neither a realistic option.

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3.4.4 Generalised costs: costs and quality of service aspects together

Generalised costs are the sum of transport costs as paid by the users of transport services and allquality of service aspects expressed in monetary form. Costs and quality of service aspects can be

taken together in monetary terms by applying the value of time. From studies in the Polish context

it appeared that a delay of one day of one TEU is valued at USD 50. Thus the generalised cost

difference for the Warsaw region comes at 209 + 4.7458 x 50 = USD 28.2. See also table 3.2 and

the all-or-nothing allocation based on generalised cost leads to a total throughput for Gdynia of

29,200 TEU. See column 7 in table 3.2.

3.4.5 Specification logit model

The following specification of the Logit model is used:

Dij: dummy variable indicating whether or not customer j has a preference for routing i;

Ci : shipping costs for routing i inclusive of freight rate, handling charges, land transport costs

etc.

Ti: transit time for routing i

0ij,1ij,2ij: coefficients of utility function

The values for the coefficients of the utility function are:

0ij= 0.00;

1ij= -0.01;

2ij= -0.50;

3.4.6 Results of alternative choice models

The container flows to and from the various Polish regions can be allocated to the two routings

according to different criteria such as:

1. All-or-Nothing (AON) based on costs only: this leads to 55,700 TEU allocated to the

Gdynia routing. See column 5 in table 3.2;2. All-or-Nothing (AON) based on time only: this leads to not any container allocated to the

Gdynia routing. See column 6 in table 3.2;

3. All-or-Nothing (AON) based on generalised costs: this leads to 29,200 TEU allocated to the

Gdynia routing. See column 7 in table 3.2;

4. Logit model: this leads to 29,500 TEU allocated to the Gdynia routing. See column 8 in

table 3.2, which is rather close to AON based on generalised costs. Comparison of the

columns 7 and 8 shows that per region the differences of the allocated flows are rather big.

(3.7) T+C+D=U i2iji1ijij0ijij

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The values of the explanatory variables are given in table 3.2.

Table 3.2 Application of alternative container flow allocation criteria

Region flows -cost -time -cost market shares Gdynia routing in 1000 TEU

1000 TEU USD/TEU days - time

USD/TEU

AON costs AON time AON costs Logit costs

+ time + time

(1) (2) (3) (4) (5) (6) (7) (8)

Warsaw 8,0 -209 4,7 28,2 8 0 0 3,4

Lodz 8,1 -37 5,0 214,3 8,1 0 0 0,9

Krakow 7,5 75 5,2 336,9 0 0 0 0,2

Wroclaw 10,9 254 5,5 527,1 0 0 0 0,1

Poznan 6,9 188 5,3 454,4 0 0 0 0,1

Gdansk 6,3 -331 4,5 -105,4 6,3 0 6,3 4,7Szczecin 4,6 261 5,4 531,1 0 0 0 0,0

Torun 6,1 -194 4,8 44,3 6,1 0 0 2,4

Katowice 14,5 88 5,2 350,1 0 0 0 0,4

Radom 4,3 -212 4,8 27,0 4,3 0 0 1,9

Lublin 12,4 -243 4,7 -6,1 12,4 0 12,4 6,4

Bialystok 10,5 -409 4,5 -182,8 10,5 0 10,5 9,0

Total 100,1 55,7 0 29,2 29,5

3.5 Market share of the container port of Rotterdam

3.5.1 Introduction

For their imports and exports of containerised cargoes the various parts of the European mainlandcan be served by different combinations of deep-sea routes, seaports and modes of transport.Certain parts of the West European hinterland can be served by up to 200 different combinations, orroutings, and practice is that certain parts of the hinterland are served by routings using each of theseaports in the Le Havre Hamburg range. Formulated from the point of view of the ports, thereappears to be a great overlap between the hinterlands of the North Sea ports. This also demonstratesthe great amount of competition that exists between these ports.

The great degree of competition makes it difficult to measure the impact on the relative position

of ports through changes in costs and quality of service: for instance, to measure the impact of

changes with respect to access to the sea (the ability to accommodate bigger ships), productivity

of handling cargo and ships and access to the hinterland by road, rail or waterway.

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The sea freight of the deep-sea trades has no influence on the cost variable in the logit function,

as the liner shipping services apply the same tariff to each of the continental seaports. In the

statistical analysis on the choice of transhipment port, the ports of Southampton, Felixstowe and

Le Havre are added. For the UK ports, alsoa difference in ocean sea freight has to be adopted,as they face a higher deep-sea sea freight

17.

For the container flows generated by the continental regions a number of models were tested.

See table 3.3. In the first model the variables, except the reliability of service variable, as given

in equation (3.5) were adopted. All except the time variable showed significant18

values. Most

probably, the low significance of the time variable can be explained by the correlation, which

exists between the time and the cost variable.

The inter-arrival time in port variable is an index representing the average number of services

calling and is proportional to the average number of weekly services calling at a port of the

TransAtlantic and Europe Far East trade routes. The variable appears to be significant. The

value of the inter-arrival time variable cannot be compared with the time variable. For a port

with 10 lines calling weekly on the Europe-Far East trade route, the inter-arrival time value

would come at 365/(10*52) = 0.7 days. It is not clear what this time stretch means for a

particular shipper/receiver and what he considers important for his choice: the number of calls

offered by an operator, an Alliance, a consortium or a combination of these? The number of

services connecting with the particular ports in the Far East he is trading with? The inter-arrival

time variable catches the fact that a particular port offering a great number of services is thereby

attractive. The variable therefore rather reflects the quality of service of the port.

The variable inter-arrival time of hinterland modes also appears to be statistically significant.

Various model versions were tested and it appeared that importers and exporters located further

from the ports had a different perception of the time and cost related factors than the ones

located nearby. Therefore, these variables were split in a distance dependent and independent

part, leading to a better fit. See equations 2 to 4 in table 3.3.

A further improvement appeared to come by replacing the inter-arrival time in port variable,

being a proxy for quality of service, by the market share of ports in the total. See equation 3.

The inclusion of mode specific dummy variables appeared to lead to further improvement and

has therefore been used to prediction purposes. See equation 4.

17Due to the weaker position of shippers and receivers vis vis shipping companies in the UK.

18In this article significant referes to the situation where the coefficients estimate differs with from zero with a 95% probability,

i.e. with a t-value of more than 2

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Table 3.3 Equations tested for routing choice with respect to continental hinterland regionsEquation 1:

VariableCoefficient Std. Error T-statistic

Costs-0.001632 0.000234 -6.972932

Time-0.117883 0.126456 -0.932200

Inter arrival time in port-0.510490 0.043528 -11.72785

IAT hinterland modes-0.817722 0.297808 -2.745798

R-squared0.124244

S.E. of regression 2.307671

Adjusted R-squared0.114690

Observations 246

Equation 2:

VariableCoefficient Std. Error t-statistic

Costs0.001403 0.000593 2.367766

Costs per avg. distance.-0.922787 0.195298 -4.725034

Time-0.191850 0.118327 -1.621354

Inter arrival time in port

0.076896 0.113861 0.675351IAT port per average distance-166.9038 33.06027 -5.048472


R-squared0.245657

S.E. of regression2.149571


Observations 246

Equation 3:


Costs0.001368 0.000610 2.243165

Costs per average distance-0.916726 0.196498 -4.665312

Time-0.252337 0.116732 -2.161678

Market share hub port -3.480221 2.427636 -1.433584

Market share port per averagedistance

4018.790 708.3892 5.673139


R-squared0.250777

S.E. of regression2.142264


Observations 246

Equation 4:Variable

Coefficient Std. Error t-statisticCosts

-0.000801 0.000441 -1.814973

Costs per average distance -0.675210 0.136669 -4.940489

Time 0.057925 0.123675 0.468362

Market share hub port -3.219505 1.691011 -1.903893

Market share port per average

distance

2756.291 500.1990 5.510388

Dummy rail -3.442757 0.197851 -17.40079

Dummy IWT -2.195699 0.569278 -3.856987

Dummy IWT R-WB 2.622217 1.539098 1.703736

R-squared 0.644217S.E. of regression

1.481688

Adjusted R-squared 0.635027 Observations 246

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Transhipment flowsSimilar regression analyses were conducted separately for the transhipment flows between the

seven hub-ports (four continental North Sea hub-ports, two UK transhipment ports and Le

Havre) and 14 West European transhipment areas, together generating 88% of Rotterdamstranshipment cargoes. The transhipment flows were assessed on the basis of incoming and

outgoing transhipment flows through these seaports and feeder service call patterns connecting

these ports and areas. Feeder service freight rates were collected through interviews with

representatives of shipping lines and freight forwarders.

Feeder flows between continental hub-ports were discarded. The costs of these flows are not

charged directly to shippers and receivers, as they are included in the ocean sea freight. Feeder

costs for ports outside the continental port range, however, are charged to the shippers/receivers

on top of the ocean sea freight.

For the choice of transhipment port similar equations were tested as for the choice with respect

to continental hub-port. The equations with the difference in costs, the difference in costs

divided the average distance to the hub-ports and the market share of the hub-ports showed good

results.

Table 3.4 Equations tested for routing choice with respect to overseas hinterland regions


Costs-0.0014 0.0010 -1.42

Costs per average distance-3.0394 0.9787 -3.11

Market share hub port5.7660 0.6573 8.77

R-squared0.7117

S.E. of regression 0.6400


Observations 43

Conclusion on statistical testsThe tests clearly show that the explanatory variables of differences, both in absolute terms only

and when also divided by distance, with respect to costs, frequency index and port market shares

in various forms are statistically significant. The impact of differences in time is weak and the

most likely explanation is that differences in time and cost are strongly inter-correlated.

The routing choice variables discussed so far correspond with the usual variables in a transportdemand choice model. The market share variable, however, has a special meaning. At one side,

it corresponds with frequency of service or inter-arrival time. At the other side, it includes

aspects related to the supply side. Shipping companies have good reasons to call more at a port

because of its relatively low port call costs (deviation costs mainliner, cargo-handling costs,

accessibility for large ships together cause differences in port costs). In their interactions with

shipper and receivers shipping companies therefore may call more at these low cost ports than

what would be required, considering hinterland costs and quality of service aspects only. Price

setting practices and market forces make that shipping lines do not discriminate between hub-

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ports (on the West European hinterland19

). If a certain hub-port is more attractive for them, they

just call more often at these ports and accept the higher inland costs. The common practice of

carrier haulage offers them the instrument to achieve their preferred frequency of call pattern.

3.5.3 Application of the model

The market share of the port of Rotterdam as a function of priceThe coefficients estimated were used for the prediction of container throughput. The future

throughput for the port of Rotterdam can be assessed as a function of cost differences for the

situation that the Maasvlakte-2 (MV-2) project is executed and for the situation that it is not. By

increasing the costs of the port of Rotterdam vis a vis its competitors containers are re-routed

through the competing ports. Thus, a demand curve can be constructed. By systematically

increasing the costs of containers passing through the port Rotterdam, the market share of

routings via Rotterdam becomes smaller. By setting this market share equal to the future supply

of the port of Rotterdam without the Maasvlakte-2 project, one finds a basis for the assessment

of economic benefits related to inland transport cost savings of this project20

.

Results of the use of the above models21

show that by increasing the port costs of the port of

Rotterdam vis vis comp

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Mel Course 2003 Ecorys