Forecasting rail freight demands in a fast developing society D. Menichetti1, U. Mosimann2

1Research & Transport Planning Specialist, Etihad Rail, Makeen Tower (Ajman Bank Bldg.) - 10th floor, Tourist Club Area, P.O. Box 989, Abu Dhabi, U.A.E.; +971 (0) 504437341

2Strategy& Performance Director, Etihad Rail, Makeen Tower (Ajman Bank Bldg.) - 10th floor, Tourist Club Area, P.O. Box 989, Abu Dhabi, U.A.E.; +971 (0) 509196331

Abstract

Etihad Rail, as part of the Gulf Cooperation Council (GCC) wider network, is developing a 1200km mixed passenger and freight railway linking the principal centres of population and industry as well as maritime hubs of the United Arab Emirates (UAE) intending to create a safe, efficient and sustainable transport system to form a brand new transport mode opportunity with strategic and economic as well as social and environmental benefits.

Assessment of competiveness between alternative transport modes and traffic demand forecasting constitute underlying inputs for shaping size, performance and functions of the rail business.

Unlike more conventional situations where observations and established methodologies are available, in a context characterised by a very rapid development growth, the unfamiliarity with the rail mode, the lack of existing rail systems in operation, a strong emphasis on the use of road vehicles boosted by low fuel costs and an extremely aggressive climate and environment, cost assessment and demand forecasting represent a challenge.

Gaining from the experience in the United Arab Emirates, this paper explores and highlights the general requirements and challenges in developing a modelling system to assist with the planning and implementation as well as with the commercial strategy of the railway in a fast developing society.

Focussing on the freight component, the paper identifies demand and supply characteristics to be considered and explains the specific modelling challenges outlining the proposed approaches for solution. Key considerations are the quantification of intermodal and bulk commodity demands, the determination of operation and maintenance costs as well as the verification and maximisation of economic performance of the rail system.

Keywords: Transport planning, Railway, Freight Demand Forecasting, Performance

1. Introduction

With a total estimated investment of $11 billion, Etihad Rail, as part of the Gulf Cooperation Council (GCC) wider network, is developing a 1200km mixed freight and passenger railway (Figure 1) linking the principal centres of population and industry as well as maritime hubs of the United Arab Emirates (UAE) intending to create a safe, efficient and sustainable transport system to form a brand new transport mode opportunity with economic as well as social and environmental benefits.

Fig. 1. The Etihad Rail mixed freight and passenger railway network 

Etihad Rail intends to become not only a source of national pride but also the most trusted mode of transport across the UAE providing a safe, efficient and sustainable transport system forming a completely new logistics opportunity with the overarching strategic purpose of supporting trade and industry, connecting the Gulf region and creating jobs.

The development of the rail is a first in the region and Etihad Rail has the exciting opportunity to conceive an extensive heavy rail network and its operational details from scratch.

When developing a brand new rail network, there are multiple unknowns on the future system performances and decision makers depend on modelling and simulation to identify the main opportunities and risks of each of the rail system components.

Since the 1960s, transport models, mathematical representations of traffic patterns and of the behaviour of end-users of transport systems, have assisted the planning, engineering as well as the strategy disciplines. Over the decades, transport models have evolved in complexity and they have proven to be effective tools for forecasting the effects of policy and investment in an

uncertain future. However, their real-life validation is limited by the scope of transport issues typically occurring in areas where observations are possible.

In the Gulf region, the scarcity of previous rail experience, lack of real-life operations, the fast pace of developments, the continuous and rapid changes in planning scenarios, and ultimately the unfamiliarity with the rail mode, challenges the conventional modelling approaches resulting in the need for adaptation of current best practice techniques to unknown conditions.

Gaining from the experience in the United Arab Emirates, this paper explores and highlights the general requirements and challenges in developing a modelling system to assist with the planning and implementation as well as with the commercial strategy of a brand new railway system in one of the fastest developing societies in the world.

2. Modelling freight rail: understanding of requirements

Traffic demand forecasting constitutes underlying input for shaping size, performance and functions of the rail business. Whereas in conventional situations much of even the future demand for and patterns of travel can be observed directly or extrapolated from the existing situation, in the UAE, the structure of the rail network is built up from scratch with no previous heavy rail experience in the region.

With no operational example from which to estimate the choice behaviour, demand must be built up from first principles, based on transport modelling practice developed in conventional situations, but adapted to the local environment.

An essential role for the model is to forecast what the UAE and the wider Gulf region are expected to look like in terms of the transport demands of the future, and the driving factors behind these, such as the geography and nature of goods consumption, the development of new ports and industrial areas, changes in trading and transport opportunities and such like.

Following the estimation and quantification of this most likely future, the effectiveness of alternative options can be compared. Specifically, recognising the overarching objectives of safety, sustainability, and efficiency, the model needs to be designed in a way which allows practical quantification and verification of performances for each scenario in order to assist decision makers in researching the optimal solution.

In this environment, the following list identifies the key modelling requirements:

Forecast changes in freight demand patterns and traffic conditions in 10 and 20 years Reproduce the decision making process and alternative supply chains (e.g. direct deliveries,

delivery through collection points and/or distribution centres, etc.) for the different potential freight markets and commodities.

Estimate costs and time and other relevant deciding factors including their weights in the choice of the alternative transport options

Assess the Operation & Maintenance costs for the rail and the other modes supplementing the rail mode or in competition with it

Quantify the economic and environmental benefits resulting from the rail development (e.g. reduction of trucks on the roads, increased safety, reduction of carbon emissions, etc.)

Assess the impacts on rail share and business case of alternative policies, regulations, planning scenarios, service levels, pricing levels and network alignment options

Represent spatially individual customers and their connections with the road, rail and barge networks

Evaluate the business case for sidings/branches from the main line to connect directly individual or groups of customers

Summarise the results with easy-to-understand performance indicators and user-friendly maps accessible to non-experts

Feed easy into more detailed analysis downstream in the design process

3. The components of the model system

3.1 The Etihad Rail Strategic Traffic Forecasting Model

The freight demand forecasting model is part of a wider strategic multi-modal traffic forcasting model that Etihad Rail has developed to forecast future travel demands of both passengers and freight (Figure 2).

Fig. 2. Characteristics of the Etihad Rail strategic multi‐modal traffic forecasting model 

With a total of 1153 internal zones, the model covers in detail the areas of the seven emirates of the United Arab Emirates. Additionally 18 external zones are used to reflect the UAE interactions with the other GCC countries.

The model, coded in Cube software, interfaces with a multi-modal network representing all the existing and planned strategic highway, public transport and freight schemes for the base year 2009 and future years 2020 and 2030.

Given the number of dimensions and policy options that the model requires representing, plus the inter-dependencies of passenger and freight modes in the delicate demand-supply interaction mechanism, the complexity of an integrated multi-modal model cannot be avoided.

Highway Model

Passenger Model Freight Model

Integrated strategic multi-modal traffic forecasting modelpassengers and freight demand forecasting through a ‘What-if?’ Analysis

Iterative approach including effects of inter-dependencies of passenger and freight modes

National Model

7 Emirates + Other GCC countries as externals

Total 1171 Traffic Zones

Cube Software Comprehensive Surveys Modelling years

Market study

> 8000 RSI

71 ATC locations

>900 SP surveys 2009

2020

2030

Base Year

3.2 Freight Model Overview

The objective of the freight model is to identify and forecast the type and volumes of freight movements that are likely to use the rail system for all or part of the journey.

Since the choice behaviour between modes and their respective performances cannot be observed or extrapolated from real-life operations, to allow a sufficient representation of these movements and their characteristics, it is not feasible to employ simplified vehicle-based approaches and it is necessary to introduce more complex commodity-based methods allowing detailed representation of individual volumes and the way they are or could be transported when the new rail mode will be introduced.

In order to effectively compare different transport alternatives, these methods must include a synthetic representation of the performances of each leg of the journey including hauling, handling equipment, mode transfers, distribution centres, collection points, vehicles and border crossings.

While performances of existing supply chains can be extrapolated from the present conditions, the ones involving the rail mode cannot, and thus have to be studied more carefully.

Figure 3 summarises the scope and key characteristics of the freight model.

Fig. 3. Scope and characteristics of the freight component of the Etihad Rail traffic forecasting model 

In order to allow a robust representation of the potential market opportunities for the rail the model has to be disaggregated in relevant market segments.

The model segmentation was designed from an extensive market study aiming to understand the freight volumes and the way they are moved in the country today. It must be noted that avoiding to undertake a market study well before the model development stage would likely lead to inaccurate model design and representation of results.

Extensive 2009-2010 freight traffic surveysRoad Side Interviews>8000 truck drivers interviewed

Market study including >100 stakeholders interviews

Automatic Traffic Counts Video recorded at 71 sites

ValidationThe interviews cover 54% of total domestic truck traffic volumes moving on the UAE roads (with origin, destination, commodity, volume and travel time explained)

Satisfactory validation of base year model: ±3% total variation over all screenlines

O&M truck cost model validated with industry stakeholders

O&M rail cost model internationally benchmarked

Mode choice model behavioral parameters internationally benchmarked

Automatic sidings generation

Automatic elimination of movements not generating minimum rail service

Detailed mode choiceLimited to land-based modes

Competition with feeder vessels and barges not yet been included

Commodity-based analysisdetailed to individual market opportunities

Zoning system representing precise locations (e.g. an individual quarry, factory, etc.)

Highly Disaggregated Current Limitations

The market study undertaken in the UAE revealed that the potential rail freight market can be categorised in the following two macro-segments:

Intermodal Traffic Bulk Traffic

The macro-segments are further sub-segmented by different handling characteristics and commodity type as shown in table 1.

Table. 1. Freight Model Segmentation

Now it is important to recognise that each of the sub-segments are typified by different supply chains as well as vehicles and handling equipment specifications and performances, hence they must be handled with separate modelling methods.

In order to deal with these differences, the model is divided in two major components:

1. General container model 2. Commodity model

The general container model (Figure 4) handles the movements of various goods transported in ISO containers (typically 20 feet and 40 feet containers) while the commodity model (Figure 5) deals with the bulk commodity movements from a single origin to a single destination (point to point), or movements that are distributed from bulk terminals (depot) to their final destination and on movements characterised by large and stable volumes of commodity suitable for container transport (commodity in containers).

Macro‐segments Handling Commodity

Intermodal Traffic

General Containers Various goods

Commodity in containers (Bulk commodities typified by large volumes

and suitable for container transport)

PolymersWaste

CeramicsHay

Sugar

Bulk Traffic

Depot Distribution

Sand*Aggregates*

CementSteel Finished Goods

Steel Scrap

Point-to-point

AluminiumClinker

Iron OreSteel Billets

*Point-to-point in some special cases (e.g. input to cement factory, export through port, etc.)

Fig. 4. General container model structure overview 

Fig. 5. Commodity model structure overview 

2009 Container Flows from

Surveys

Growth and Redistribution

Factors

Future Year Zone to Zone

Container Flows

Truck and Rail Transport Networks

Cost and Time Parameters

Truck Times and Costs from Initial

Origin to Final Destination

Rail Times and Costs, allowing all Terminal to

Terminal Movements and including Truck Access

Modal Split Procedure

Logit Model

Terminal to Terminal Container Flows

Remove services notgenerating sufficient

volumes

Have Services Changed

?

Output Final Services, Volumes,

Revenues and Contributions

No Yes

Rail Times and Costs based on

Restricted Services and including Truck Access

2009 Commodity Flows from

Surveys

Growth Factors and Additional

Movements

Future Year Zone to Zone

Commodity Flows by Group

Truck and Rail Transport Networks

Cost Parameters

Truck Costs from Initial

Origin to Final Destination

Rail Costs, with and without Sidings and

including Truck Access as appropriate

Have Sidings

Changed?

Calculate Movements for Individual Commodities based on Group Sidings

and Output

No Yes

Initial Siding Calculation based on

all available Movements using Rail

or Fixed Inputs

Calculation of Rail Movements Captured

and resulting Cost Savings

Recalculation of Sidings Based on

Captured Movements

Nevertheless, although different in some technical aspects (see paragraph 3.4), all proposed methods follow a similar underlying approach (Figure 6).

Fig. 6. Freight model approach 

3.3 Base Year and Future Year Demand

First the model consolidates the identified potential rail market opportunities into demand matrices, then, for each of the future years of analysis, it projects these demands into the future taking into account expected markets growth (Figure 7), changes in dynamics and geography of trading as well as incorporating other potential future markets opportunities.

Fig. 7. Illustrative example of UAE market growth assumptions for general containers 

Data collection

Model parameters definition

Base year demand

Future year demand

Freight mode choice

Economic performance

Market Study

Road Side Interviews

Automatic Traffic Counts

Land-Use data

Zoning system

Multi-modal network

Travel behaviourdata

Demand management and

policy data

O&M unit costs

Base year demand matrices

consolidation

Trafficvalidation across

screenlines

Growth assumptions

Future distribution

Forecast year potential freight demand matrix

Costs from O&M cost model

Travel times and distances from

multi-modal network

Rail Share

Rail volumes

Rail revenue

Rail contribution

Saving of truck trips

Saving of truck-km

Saving of road congestion delays

Saving of carbon emissions

Saving in road accidents

Forecast

-10%

-5%

0%

5%

10%

15%

20%

25%

2000 2002 2004 2006 2008 2010 2010-

2020

2020-

2030Years Low Case Base Case High Case

2010-2020 6% 8% 9%

2020-2030 4.5% 6% 8%

Assumed growth in container trafficAnnual growth in %

Low Case

Base Case

High Case

Standard outputs of the base year and future year model are, respectively, the consolidated 2009 freight demand matrix (in tons or TEU) and the total, over all possible modes, forecasted potential rail demand matrix (in tons or TEU).

3.4 Mode Choice Model

Once this likely future demands have been estimated, through a mode choice model which compares the costs and performances of alternative supply chains and/or routing options, the model assesses, per each movement, the probability of using rail for all or part of the journey.

The transport time and distance elements of single legs of the journeys, including hauling-time, congestion delays, mode transfer time, loading/unloading times, etc. are handled with a multi-modal network model of the UAE representing the planned highway and rail infrastructure.

The freight multi-modal network (figure 8) constitutes of:

A zoning system defining the points at which freight movements start and end. Zones can represent individual assets such as quarries, factories, ports, geographic areas within the UAE, or surrounding countries

A multi-modal network model of the UAE representing the highway and rail infrastructure, which allows times and costs to be computed for all movements.

Container and bulk terminals at which container and commodity movements can transfer between the highways and railway

Fig. 8. Multi‐modal network (2030) in the CUBE model 

Legend

Highway system

Primary

Secondary

Local

Railway system:

Rail Network

Bulk Terminals

Container Terminals

The transport costs are measured through unit costs derived from separate Operation & Maintenance costs models (see paragraph 3.5) while other not measurable deciding factors such as reliability, flexibility, safety, etc. are taken into account in the model through bias factors.

Standard outputs of the mode choice model include rail volume, rail share, revenues and contribution levels for every selected movement and sub-segment, which can then be used for further downstream analysis in the design process as well as to inform and provide directions to the sales strategy and pipeline.

At present, the model only includes competition between land-based modes (truck vs. rail), while potential to compete with transhipment on feeder vessels has not yet been analysed in detail on the grounds that numerous international benchmarks reveal that rail is unlikely to be a competitive mode against coastal water transport.

Nevertheless, for the peculiar characteristics of the Gulf region rail might play a role in attracting away demand from the barge market and the Etihad Rail traffic model is currently being extended to include the capability to evaluate this potential additional market.

3.4.1 General container model

The process of the general container mode choice model consists of several stages as shown in figure 4 and described below:

1. Assumes that services are between all pairs of container terminals and computes costs and times for zone-to-zone container flows by rail, using truck access as needed with associated transfer costs. Also computes zone-to-zone costs using direct truck transport routes

2. Uses a probabilistic Logit model (Figure 9) to split movements between rail and road paths as a function of time, cost and access type

3. Compares volumes for each terminal-to-terminal pair against those needed to justify a regular service

4. Restricts the rail network to movements between terminal locations that have sufficient volume

5. Computes rail and road costs on the restricted network and re-applies the probable model to determine final container volumes for each service.

Fig. 9. The general container mode choice model uses a binomial Logit formulation to split movements between rail and road paths as a function of time, cost and access type 

Given its probabilistic nature, the model is designed to assess the impacts on travel patterns and revenues when the structure of rail tariffs change and, in turn, can be used to evaluate the optimum points for revenue and contribution. Figure 10 shows the results of this analysis.

Fig. 10. The model is also designed to assess the impacts on travel patterns and revenues when the structure of rail tariffs change 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900

% T

ruck

Sh

are

% R

ail S

ha

re

Distance (km)Rail Share(Container - Direct) Rail Share (Short trucking at one side) Rail Share (Short trucking at both side)

Assumptions Rail speed: 90km/h

Truck speed: 60km/h Mode transfer time: 6 hours

Truck costs assume 20% contribution

Rail costs assume minimum contribution

0

500

1000

1500

2000

2500

3000

0

5000

10000

15000

20000

25000

0% 20% 40% 60% 80% 100% 120%

Ann

ual R

even

ue a

nd P

rofi

t (m

illi

on A

ED)

Ra

il V

olum

e -

Dai

ly T

EU

s

Contribution margin

Volume (TEUs) Revenue Contribution

Max RevenueMax

Contribution

3.4.2 Commodity Model

The commodity rail model is designed to forecast bulk freight movements that are expected to use the rail system for all or part of the journey. It concentrates on movements of sufficient size to justify running a unit train consisting of a single commodity for which the exact location of the producer or of the consumer (or of both) is known.

This model handles the following movements:

movements from a single origin to a single destination (point to point), movements that are distributed from bulk terminals to their final destination or collected

from multiple origins at collection terminals (depot) movements characterised by large and stable volumes of commodity suitable for

container transport (commodity in containers)

For all of the above groups of movement, a key consideration in the computation of rail transport costs is the way that the commodities access the rail system. Therefore, it is vital that the model is able to compute whether a direct access siding can be justified or whether truck access will be more economical.

The steps of the commodity mode choice model process for each commodity and origin-destination pair, as shown in Figure 5, is described below:

1. Compute the potential savings for a zone/commodity group combination which is accrued from the provision of a siding, assuming initially that all such movements would use rail. Assign a siding, if justified

2. Compute truck cost for performing the movement, including line-haul, loading and unloading costs from the highway network. An empty back-haul movement is always assumed

3. Compute the terminal characteristics of the rail movement, based on the siding provision. At either end, there may be direct access to the rail system and truck feeders/de-feeders may be required, which have a major bearing on the rail cost

4. Compute the costs of the rail movement, including rail line-haul, loading and unloading, plus any necessary truck movements with the associated loading and unloading costs. For some commodities, a final truck movement is needed from a depot location. If the total rail movement cost1 is less than the road movement cost, then the volume is included as a potential rail movement (All-or-Nothing approach) as shown in the example in figure 11.

5. Compute sidings based on the movements captured by rail in Step 4. If this is unchanged from the previous siding set then the process is complete. Otherwise Steps 3 and 4 must be recalculated until no further sidings are eliminated.

1Rail movement cost consists of the complete variable costs of moving a commodity by rail. It includes crew costs, depreciation and maintenance for the locomotives and wagons, energy costs, signalling, infrastructure maintenance, and the SG&A (selling, general and administrative) expenses.

Fig. 11. Costs of Rail Transport vs. Truck – Illustrative example for aggregates & sand 

The commodity model is characterised by a very detailed zoning system representing precise locations, consisting, for example, of an individual quarry, factory, or similar specific point (Figure 12).

Fig. 12. Detailed zoning system representing precise locations, consisting, for example, of an individual quarry, factory, or similar specific point 

0

5

10

15

20

25

30

35

40

45

50

10 30 50 70 90 110 130 150 170 190 210 230 250

AED

/Ton

Distance (km)

Rail Cost (truck feeder + extra handling) Rail Cost (through distribution centre)

Truck Cost30 Tons Capacity

Truck Cost60 Tons Capacity

For depot movements, characterised by an additional truck handling from the distribution centre to the end-consumer, the model distributes the volumes to the surrounding geographic areas in accordance with land-use densities.

It is important to recognise the importance of this detailed zoning system which allows an accurate calculation of access costs to the railway from precise locations. With a more conventional zoning system constituted by geographic areas only, the All-or-Nothing process explained above would not be able to provide accurate outputs.

3.5 Operations & Maintenance (O&M) cost models

Detailed parameterized cost models for both rail and truck transport were developed.

The trucking costs are calculated for five different truck types used for different commodities using specific sets of parameters for each. The sensitivities of varying yearly mileage, technical life, etc. were tested and the model results for trucking costs were verified by benchmarking them against both market rates paid by shippers today as well as cost data collected from shippers with own truck fleets.

The trucking costs model is based on the formulation used by the Highway Design and Management (HDM) Model developed by the World Bank (IBRD) which recognises that trucking costs increase when the average truck hauling speed decrease, for example as an effect of increased congestion.

The model takes this into account by creating speed-cost curves (Figure 13) for each truck type which are then used in the mode choice model to compute route costs based on actual congested speeds derived from the multi-modal network.

Fig. 13. Trucking Cost Model ‐ Speed Cost‐Curve illustrative example for container truck 

Vehicle Operating Cost = VOCa * (Speed)VOCb

9.08

6.39

4.94

3.99 3.28

2.84

2.54

2.31

2.14

1.99

1.88

1.79

1.76 1.76 1.76 1.76 1.76 1.76

y = 59.609x-0.838

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

0 20 40 60 80

Veh

icle

Ope

rati

ng C

ost

in

AED

/km

Speed in kilometers per hour

Assumptions Include 20% profit margin

Do not include the empty back trip

Rail costs are also calculated with a detailed bottom-up cost model, parameterized for different wagon types, train lengths, etc. for the various commodity segments. The results for various cost categories such as rolling stock maintenance and infrastructure maintenance as well as KPIs like yearly loco mileage, drivers per traction unit, etc. were benchmarked against international rail operations.

In addition to the line haul costs, the operating and maintenance costs of rail terminals had to be calculated. For this purpose, a number of terminal configurations for small, medium and large terminals and with different handling technology depending on the commodity type were developed. Again the costs were then calculated bottom-up based on handling equipment’s fuel consumption, operating personnel required, etc.

The rail input cost parameters were randomised in the model through a Monte Carlo Analysis (using Palisade @Risk software) to obtain cost ranges at defined levels of confidence. This was done in order to obtain a quantification of the risks connected to the uncertainty and potential high variability of some of the costs parameters in the unknown conditions.

One important thing to consider is that while trucking costs are mostly variable because the road network and its maintenance is considered “free” from the truck’s point of view, the rail costs have to be calculated for the different contribution levels. For the mode choice, it was then assumed, that the railway would offer a rail service only, if it can cover its variable costs. The difference between the rail tariff (which is assumed to be equal or slightly below the truck tariff) and the variable rail costs, are then the contribution earned to cover the railway’s fixed costs and amortize the infrastructure investment. This does of course assume unlimited network capacity and the results will therefore need to be tested for feasibility on, in our case, the given double track network.

3.6 Economic performance model

The rail mode, as a brand new transport mode in the region, has the opportunity to provide a positive change to the way passengers and freight move across the country boosting the economy and providing a sustainable transport alternative.

Sustainability concerns and an overall desire to increase road safety, reduce the carbon footprint, and improve the level of service of the transport network.

Economic and sustainability performances are primary objectives in the development of the Etihad Rail network, hence, when planning and designing, it is necessary to quantify and compare the effectiveness of alternative options in this regards.

Undertaking this analysis in great detail is particularly important in a society unfamiliar with the rail mode and where this way of transport needs to prove itself as effective for a society markedly road-vehicle based. Moreover, it allows more informed planning decisions and, overall, it significantly reduces the risks of a design not in line with its vision, mission and objectives.

Recognising the importance of this aspect, the Etihad Rail traffic model, taking inputs from the mode choice models, summarises these performances measuring, for each rail movement, the beneficial impacts introduced by the rail network through the following performance indicators:

Saving of truck trips Saving of truck-km Saving of road congestion delays Saving of carbon emissions Saving of road accidents

This allows decision-makers to test multiple scenarios and identify which network configuration and market strategy can maximise the above benefits, to locally identify which are the best performing rail services and also to investigate which services, even if not competitive on a pure financial basis, may be fostered through subsides.

4. Data requirements

Modelling a freight rail system requires, in addition to a complex suite of modelling tools, a considerable amount of input data. These can be categorised into the following set:

4.1 Market study data

In order to build a solid understanding of the freight market, undertaking an extensive market data collection from industry stakeholders deemed likely to benefit from or influence future demand for rail freight, cannot be avoided.

The study provides an order of magnitude assessment of current freight volumes that could be converted into rail freight in the future and need to be undertaken prior the model development phase.

The interviews should focus on traffic volumes of the major originators of freight, supply-chain providers, and government entities, with accountability for vision, policy and strategy development, and implementation. Interviewers should also seek information on future market growth to validate the future year projections of volumes. This information forms the base data set on volumes for each commodity flowing between origins and destinations and represents a vital input to the model.

4.2 Traffic Surveys data

Traffic surveys are necessary to capture the types of vehicles, magnitude as well as time and spatial distribution of volumes throughout the day. They are also required to validate model responses against observations (i.e. modelled traffic flows over corridors, travel time and trip length between zones).

Typically, the traffic survey set should include automatic traffic count (ATC) complemented by roadside interviews (RSI).The results of the RSIs for dispersal of freight trip origins and destinations are factored against the ATCs to develop the movement sampling.

The coverage of the RSIs compared to the ATC counted container movements should be at least 35-40% to provide a good statistical base for allocating movements.

4.3 Travel behaviour data

Travel behaviour data reflects mathematical relationships and associated parameters describing the travel choices of end users of the freight rail services, balancing the pros and cons of alternative modes or combination of modes, destinations, routes and other deciding factors such as reliability and frequency. Whilst these can be usually observed from real-life behaviour, in the case of the UAE these parameters have inevitably been imported from studies validated elsewhere and adapted to the local environment.

4.4 Land-Use Data

These focus on the main drivers of travel demand, disaggregating the continuous nature of a region into convenient zones. Data are usually obtained from a census (in existing situations)

or master plans (in future situations). They determine the potential of each zone to generate and attract freight movements.

4.5 Demand management and policy data

Demand management can include tolls, truck bans, truck weight restrictions, diesel prices etc. This data is determined by policy and plans and can be obtained from consultation with stakeholders. As most other data sources, changes in demand management inputs can affect significantly the results.

5. Applications

The Etihad Rail Traffic Forecasting Model is a significant investment representing an essential strategic tool to inform many performance oriented decisions. Its use focuses on a wide range of design and planning issue, always recognising the need to maximise the financial and economic performance as well as to reduce risks in the development of the rail network.

Fig. 14. The model generates terminal‐to‐terminal rail demand flows   

Applications range from long to medium term planning options assisting with:

Assessment of different alignment and terminal positioning options Evaluation of impacts of different demand management and policy options Comparing costs and times of transport modes (or combination of modes) in competition to

assist with the pricing strategy Identifying impacts of different pricing levels Dimensioning of network and terminals Phasing of the network and terminals Optimisation of economic and sustainability performance of the rail network Pre-dimensioning of rail level of service

Assessment of impacts on business case of different network, policy and planning assumptions

Generation of inputs for the Benefit-Cost & Economic Analysis Identifying traffic with low financial competitiveness but high economic benefits and

quantifying for subsidy requirements Informing the sales pipeline

6. Conclusions

Etihad Rail is planned to be the first heavy railway system in the UAE to create a safe, efficient and sustainable transport system.

The challenge for delivering a brand new rail network is to provide an effective and efficient transport mode while maximising the economic and sustainability performances.

The probability of success has been maximised by employing transport modelling techniques. Their use is well-established in more conventional environments, as input to design, funding applications and environmental appraisal.

Both the absence of observations of rail operations in the Gulf region and the uncertainties connected to the large size and fast pace of new developments provide challenges to the model design, development and validation requiring a very detailed analysis disaggregated to first principles and individual market opportunities.

A tiered modelling system has been developed, integrating individual components that simulate the demand for and performance of each element of the rail system and of its complementing and competing modes.

When implemented, Etihad Rail will be a test bed for the development of commercially viable and sustainable rail solutions, and future modelling tools to support their implementation elsewhere in similar environments.

FURTHER READING

1. A Guidebook for Forecasting Freight Transportation Demand, (1999), NCHRP Report 388, RB, National Research Council, Washington D.C.

2. Bennett, Greenwood, (2003). Modelling Road User and Environmental Effects – Volume 7 of The Highway Development and Management Series. International Study of Highway Development and Management (ISOHDM), World Road Association PIARC, Paris.

3. Beuthe M., Bouffioux Ch. (2006). Analysing freight transports’ qualitative attributes from stated orders of preference, Proceedings of the European Transport Conference, Strasbourg, Seminar on Freight and Logistics. See http://www.etcproceedings.org

4. Beuthe, M., Jourguin, B., Geerts, J. F. and Koul A Ndjang' HA, (2001). Freight transportation demand elasticities: a geographic multimodal transportation network analysis, Transportation Research E, 37,pp. 253-266.

5. Cramer J.S., (1991). “The logit model”. Edward Arnold. 6. Danielis, R. (2002). Freight Transport demand and stated preference experiments

(partly in Italian), FrancoAngeli, Italy 7. IRE and Rapp Trans AG (2005). Evaluation of quality attributes in freight transport,

ASTRA Research Project No. 2002/011, Lugano/Zurich 8. Odoki, J.B., Kerali H.R.G., (2000). Analytical Framework and Model Descriptions –

Volume 4 of The Highway Development and Management Series. International Study of Highway Development and Management (ISOHDM), World Road Association PIARC, Paris.

9. See http://www.worldbank.org/ (last accessed August 2011) 10. SYSTRA/ CANARAIL/ SCEC (K&A), (2008). Feasibility Study of the GCC Railway,

Prepared for GCC Secretariat General 11. Tavasszy L.A., (2006). Freight Modelling – An overview of international

experiences, TRB Conference on Freight Demand Modelling: Tools for Public Sector Decision Making, September 25-27, Washington DC

12. Wigan, M.R., F.Southworth (2005). What's Wrong with Freight Models and What Should We Do About It?, European Transport Conference, October 2005, Strasbourg, France

13. Wilbur Smith Associates, (2011). Passenger and Freight Demand Forecasts – Final Report, Prepared for Etihad Rail, Abu Dhabi.