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Freight Modelling – An overview of international
experiences
Lóránt A. Tavasszy1

Paper prepared for the TRB Conference on Freight Demand Modelling: Tools for Public Sector
Decision Making, September 25-27, 2006, Washington DC

Abstract
Compared to passenger transportation modelling, the field of freight modelling is relatively
young and developing quickly into different directions all over the world. The objective of
this paper is to summarize the international state of the art in freight modelling, with a
focus on developments in Europe. We start with a brief description of key issues in freight
policy that create a growing demand for freight demand modelling. Some of these are
common to the freight agendas in many places of the world, some are more articulated to
the European situation. In order to build up our report systematically, we first sketch a

conceptual framework of the freight system. We identify 3 emerging areas of innovation in
freight modelling which have been driven by the European transport policy context and are
relevant for US freight policy as well: 1) freight-economy linkages, 2) logistics behavioural
modelling and 3) freight trips and networks. We describe the state of the art in these areas
and propose areas of further modelling work. We conclude the paper with a summary of
our main ideas and recommendations, including the challenge to create new data sources
about freight flows that have not been available before.
1

Lóri Tavasszy is senior advisor at the Mobility & Logistics group ofTNO, the National Institute for Applied Scientific
Research, Delft, The Netherlands and visiting professor at the Radboud University, Nijmegen, The Netherlands

1

1. Introduction
Compared to passenger transportation modelling, the field of freight modelling is relatively
young and developing quickly into different directions all over the world. As the direction of
development has depended on local priorities in freight policy, it is not surprising that
freight model development in Europe has walked a slightly different course than in the US.
The objective of this paper is to summarize the international state of the art in freight

modelling, with a focus on Europe. We try to achieve this by focusing on 3 areas of
innovation in freight modelling which have typically been driven from a European context
but are relevant for US freight policy as well:
-

Freight-economy linkages

-

Logistics behaviour

-

Freight trips and networks

There are numerous reviews of freight transport models around in the transport modelling
literature. We do not repeat them here; most of them can be found through the Freight
Model Improvement Program website. Please note also that, as we have chosen to
develop the storyline of this apper according to the above lines, we do not aim at providing
a complete set of references to all available EU work in freight modelling. Our account will

be limited to a selection of key papers in the literature. Recent freight model literature
reviews that include European experiences within an international context can be found in
Burgess (2001), Transforum (2006), WSP (2002a) and de Jong (2004).
Our paper is built up as follows. The first section introduces some definitions and provides
a rough overview of the main lines of development in freight modelling in the last four
decades. In the main part of the paper we develop a discussion line along the above
major areas of innovation in freight modelling. We conclude with a summary of the lessons
from EU experiences and sketch some perspectives for future model development.

2. Freight policy issues and modelling needs
Before we describe the main lines of model development in Europe, we give a short
description of the key issues in freight policy that have created the demand for freight
demand modelling in the first place. Some of these are common to the freight agendas in
many places of the world, some are more articulated to the European situation.

Table 1 Key policy issues and associated modelling needs
Policy Issues

Modelling Needs

Growth of freight: a doubling of freight flows by 2050, worldwide (WBCSD, 2004), is
expected. Within Europe, International flows are growing at twice the rate of domestic
flows.

Forecasting international freight growth.
Decoupling freight/economy. Sensitivity
to cost changes.

Growing freight shares on the roads: as passenger traffic growth is slowing down and
freight is moved by more and smaller trucks, freight is becoming more dominant on
the streets

Influence of freight intensities on car
drivers

2

Truck traffic behaviour

Creation of seamless multimodal networks, new focus on Motorways of the Sea and

inland waterways

Linking sea- and land transport models,
EU multimodal networks

Concerns about international competitiveness of the EU economy, two-way relation
between worldwide networks and global trade. “Freight and the economy” discussion:
what are costs and (mainly indirect) benefits of freight investments?

Develop suitable worldwide models and
continental models. Improve relation
between SCGE and network models

Pricing: Additional charging all modes of transport what they can bear (or, what is fair,
given external costs unaccounted for) is becoming reality. EU and member states
have different attitudes and strategies towards pricing.

Situational response to cost changes
(truck type, road type, time of day)

Logistic performance: the freight logistics sector is customizing its products and is
creating complex, flexible networks using advanced logistics concepts such as hybrid
supply chains, collaborative networks, e-logistics (both business-to-consumers and
business-to-business) and return logistics.

Differentiating between goods with
different logistic backgrounds; making
detailed statistics available

Changes in vehicle types HGV/LGV: light vehicle growth figures surpass other
categories and appear to be more difficult to capture (both in terms of measurement
and public policy)

Forecasting (causes and impacts of)
choice of vehicle type

Local environmental damage: new regulations on noise and emissions require more
accurate prediction of freight impacts. New technology requires investments. Citizen
involvement in freight planning.

Accuracy of forecasts and level of detail
(type of traffic, spatial, temporal)

24-hrs economy: to deal with congestion, firms are spreading production and logistics
over day and night

Explaining sprawl of flows to different
periods of the day

Security and safety: traffic needs to be monitored for degree of risk depending on
contents or origin of freight

Modelling critical global movements:
containers, oil, dangerous goods, food

City distribution: as more stern policies are developed for city access and activities,
freight requires new delivery concepts

Forecasting of tours at urban level, time
of day dependent

The table indicates that freight modelling within Europe requires: (i) a certain growing need
for detail (vehicle types, logistics, spatial detail) and (ii) an extension of dimensions of
freight modelling into the broader transport system (geographically as well as functionally,
i.e. linking transport and the economy).
Clearly the existence of the EU Common Transport Policy has fostered the development
of all kinds of EU level, international models where one has attempted to satisfy as many
of the above requirements for improvement as possible. In particular, the creation of
continental models, where domestic and global freight is intertwined, where all modes of
transport are relevant and where borders play a crucial role, has been a development
typical for Europe. Priorities of the individual countries have often developed in parallel to
EU policy and EU level research. Our focus in the remainder of the paper will be on the
main development lines that have emerged from these national and EU level research.

3. Emerging lines of model development
In order to build up our report systematically, we propose a conceptual framework based
on firm decisions relevant to transportation demand. This frame resembles the 4-step
modelling approach but allows a) to take into account the decision problems that firms
face related to freight movements b) extensions to include operations which are typically
less relevant to passenger transport, such as storage (see figure 1).

3

Production and
Consumption

Trade (Sales and Sourcing)

Logistics Services

Transportation Services

Network Services

Figure 1 Conceptual framework of the freight transport system
Since the advent of transport modelling, freight modelling has gone through a number of
major development stages, building up our knowledge in each of these layers individually,
and slowly connecting them to one another.
The first major national attempt in Europe to describe freight transport flows was in the
early 70’s (Chisholm & O’Sullivan, 1973). These models focused on the layer of trade,

using gravity modelling as a main tool. A new impetus to freight modelling was given by
the use of Input/Output (I/O) and Land Use-Transport Interaction (LUTI) models, as these
explained the interaction between trade, transport and the economy (Williams, 1977). As
behavioural modelling took up for passenger transport in the 70’s, the first mode choice
models became available for freight as well.
The 80’s were characterized by an increased interest in network modelling and extended
network models or hypernetwork models, explainng simultaneously trip generation, trade,
modal split and route choice (refs Friesz, Harker).
In the 90’s these models were extended using a multicommodity context (Crainic et al,
1990), improved probabilistic choice models and inventory considerations (Tavasszy,
1996). In the last decade we have seen an emergence of freight network simulation
(Southworth/Nagurney, Groothedde). These models have taken up the instrument of
microsimulation or network modelling as approaches to describe behaviour of various
agents in the system. Their advantage is that they are able to describe actors in detail,
while their main challenge their calibration and validation. Another and closely related new
breed of freight models aims to describe agent behaviour by including game theoretic
considerations (Thorson, 2005). These models now focus on freight exchange markets
and serve both decision makers in the private and the public world.
Table 2 summarizes these developments from the view of our system framework. The
general trend we observe over these four decades are those of 1) increasingly integrative
treatment of various decision that firms make, or layers in our conceptual model and 2)
inreasing detail of the behavioural content of models, down to the level of simulation in
responses of individual firms.

4

Table 2 Summary of modelling challenges and techniques
Decision problem

Typical modelling challenges

Production and
consumption

trip generation and facility
location
freight/economy linkage
consumption patterns
international trade
value to volume conversion

Trade

Logistics services
Transportation services

Network and routing

inventory location
supply chain management
considerations
choice of mode
intermodal transport
light goods vehicles
routing and congestion
tour planning
city access

Typical techniques employed

trip generation models, I/O (70’s)
LUTI (‘70s) and
SCGE (‘90s)
models
gravity models,
synthetic O/D
models (‘70s)

agent based
simulation
models (‘90s)

logistics choice models (‘90’s)
simple trip
conversion
factors (‘70s),
discrete choice
(‘90s)
network
assignment
(‘80s), simulation
(‘90s)

multimodal
networks (‘80s)

The main developments in freight system models we will discuss in the next chapter are
the shaded cells in the table and concern the following 3 categories:
-

-

-

Improving the representation of freight-economy forward linkages: in freight benefitcost studies, an important impact to consider is the productivity growth associated with
improvements in accessibility. These forward linkages within the economy require
models treating the function of transportation in product markets. To this end, spatial
economic models are being developed which integrate the first two levels of our
framework, trade and production/consumption. The latest addition to this set of
models are the spatial computable general equilibrium models (SCGE) models.
Logistics behaviour: freight logistics models aim to describe explicitly the trade offs
between transport and inventory holding. They build a link between origin/destination
(O/D) tables for production and consumption locations and O/D tables where
warehouse locations are included. This is relevant as it determines 1) the spatial
patterns for goods flows, changing the usage of infrastructure, 2) the costs of freight
movements and 3) the (local and global) economic impact of freight policies.
Freight trips and networks: In Europe quite some research has been done in the last
decade on multimodal network assignment for freight. These models operate at EU
and national level and have various degrees of refinement, up to stochastic and multi
user class models. At a more detailed level, the data challenge becomes daunting,
however. Models that describe the choice of vehicle type at the scale of a city or
region are virtually non-existent. The main empirical challenges lie in disentangling
ligh goods vehicle from heavier ones, and services sector from freight-only
movements.

4. International experiences in 3 areas of innovation
In this chapter we give a brief account of the main research in modelling that has occurred
in recent years in the areas mentioned above. We discuss the difficulties with adoption of

5

these innovations by their users and sketch the challenges ahead for further model
development and implementation.

Freight-economy linkages
The advent of Spatial Computable General Equilibrium (SCGE) modelling have provided a
new tool to model, in an consistent fashion, the first two layers of our systems model in
Figure 1. From an economy-wide perspective, a SCGE model is a commonly used refined
tool. This model is based on a microeconomic general equilibrium framework that allows
for substitution possibilities at the supply side (production) as well as the demand side
(consumption) of the economy, via an endogenous-price system. It takes account of
intersectoral and interregional relationships in an economy and is hence a suitable tool for
obtaining insight into economy-wide, direct and indirect, consequences of transport
policies.
In Europe, the first example of such an SCGE model was the CGEurope model developed
by Bröcker. He developed this model for 1300 regions covering the entire European space
(see Bröcker, 1998 and 2003). The main purpose of Bröcker’s SCGE model is to quantify
regional welfare effects of transport related and financial-economic policies, such as the
Trans-European Networks (TENs) investments and transport pricing.
In the UK, as well as in the Netherlands, national economic research institutes have
worked together in a research program on economic effects of infrastructure, under the
authority of the national government. Based on these findings, and built upon the work of
Venables and Gasiorek (1996), the Dutch SCGE model RAEM has been constructed and
applied (Knaap and Oosterhaven, 2000). Furthermore, European SCGE models have
been developed in Denmark (the BROBISSE model; Caspersen et al., 2000), Sweden
(Hussain and Westin, 1997; Nordman, 1998, Sundberg, 2002), the PINGO model in
Norway (Ivanova et al., 2002) and Italy (Roson, 1995). Recently a Swedish initiative was
launched to investigate the possibilities of introducing SCGE modelling as part of the
national freight model (Williams et al, 2003).
Outside Europe, SCGE models have recently been developed in the US (e.g. Löfgren and
Robinson, 1999), where relevant research has also been performed by Lakshmanan and
Anderson (2002). In Japan SCGE models have been used (see Koike et al., 2000 and
Ueda et al., 2001) to analyse the potential impact on the Japanese economy of a major
earthquake that damaged the high speed rail network to Tokyo. Miyagi (2001) has used
an SCGE model to appraise the indirect economic impacts of a large expressway project.
A logical step in model development would be to connect such a model to a model of the
rest of the freight transport system, replacing conventional I/O and gravity type
approaches. This step involves fitting the two parts of the system together in terms of e.g.
representation of the transport sector, units of measurement, time scales, study area,
spatial resolution, utility formulations, functional forms etc. Examples of consistency issues
that arise when linking SCGE and transport network models are given in Tavasszy (2002).
Clearly, the benefit of such an integrated treatment is the theoretical consistency that we
gain within the freight modelling environment. A second, though related, benefit is an
improved ability to assess of indirect welfare effects of freight transport policy. Especially if
logistics models are used, we can consider possible benefits of logistics re-organization
responses in CBA (Lakshamanan et al, 2002).
As this is a relatively young development, there have been only few aplication for transport
policy purposes. The Dutch SCGE model was applied to several benfit-cost studies

6

related to long term port and rail development (see e.g. Knaap and Oosterhaven, 2000).
The CGEurope model was used to advise European Commission during the interim
assessment of the EU White Paper on the Common Transport Policy. It provided new
forecasts of sectoral and regional development in the scenario of decelerated
development of the Trans European Network. Despite the claim that these models are
data hungry and tedious to calibrate, the fact that many countries have started to
investigate these models is a promising sign. The first challenge to solve, however, indeed
relates to the preparation of national statistics (a detailed social accounting matrix or
multiregional I/O is a sound basis) to base these models upon.

Logistics behaviour
The introduction of elements of logistics decision making in freight models took off in the
early 90’s in the Netherlands. It has taken about a decade before these or similar
approaches were starting to become adopted elsewhere. At the moment there are at least
5 logistics based freight models under development in the world, 4 of which are in Europe.
The most recent one is from the US; in 2005, a proposal for the LA County freight model
was presented at the TRB Conference (Fischer et al, 2005).
The earliest reference to logistics models we found in Bergman (1987) who proposes a
more detailed spatial representation of logistics processes in freight logistics models.
SMILE (Strategic Model for Integrated Logistics and Evaluations; see Tavasszy et al,
1998) is the first aggregate freight model developed to account for the routing of flows
through distribution centres. The model enumerates alternative distribution channels,
takes into account freight consolidation possibilities and calculates the usage of these
alternatives using a logit choice model. The model started operating in 1998 and has been
used for many policy studies since then. The introduction of the model gave a start to a
stream of new survey and modelling work in this area, within the Netherlands, but also
abroad.
At the Delft University of Technology, the GOODTRIP (Boerkamps and van Binsbergen,
1999) model was developed. The model builds logistical chains by linking activities of
consumers, supermarkets, hypermarkets, distribution centres and producers. Based on
consumer demand, the GOODTRIP model calculates the volume per goods type in m3 in
every zone. The goods flows in the logistical chain are determined by the spatial
distribution of activities and the market shares of each activity type - consumer,
supermarket, hypermarket, distribution centre, etc. This attraction constraint calculation
starts with consumers and ends at the producers or at the city borders. A vehicle loading
algorithm then assigns the goods flows to vehicles. A shortest route algorithm assigns all
tours of each transportation mode to the corresponding infrastructure networks. This
results in logistical indicators, vehicle mileage, network loads, emissions and finally energy
use of urban freight distribution.
Another application which followed the SMILE development is the SLAM model (Spatial
Logistics Appended Module; see Tavasszy et al, 2001), which was a EU-level spin-off.
The model is appended to the EU-level SCENES transport model. It obtains trade flows
(in the form of a matrix containing flows between producing and consuming regions) as an
input from SCENES and produces transport O/D matrices for the 200+ zone system in
SCENES. These O/D-tables incorporate alternative distribution chains. A chain is defined
as the combination of distribution centres (DC’s) and transport relations for trade flows
between producing and consuming regions. This second O/D table, the output of SLAM, is
then fed back into a European model for freight network model, which uses the modified

7

O/D table to determine modal split and routing of flows. This logistics module was adopted
as part of the new, standard, EU transport modelling suite, TRANSTOOLS.
A slighlty more advanced logistics module was proposed for the national Swedish freight
model SAMGODS (Ostlund et al, 2003). This proposal is now underway in its
implementation as a joint Norwegian-Swedish initiative, in an even more refined form (de
Jong et al, 2005). In contrast to the above described aggregate approaches this model
takes a mixed aggregate-disaggregate modelling approach. Here, aggregate data on
trade flows between regions are distributed over pairs of individual firms, based on various
kinds of firm attributes such as sectoral affiliation and size. The resulting disaggregate
flows are then spread over different distribution channels (and, possibly, modes of
transport) using a microsimulation approach. In the final step these flows are aggregated
again to form interregional transport flows.
In the United Kingdom, following the UK freight model review (Williams et al, 2004),
parallel to the above models, the recommendation was to distiguish in their freight
modelling framework between two types of spatial interactions: trade and transport
interactions. Data describing interactions of the first type were termed
production/consumption (P/C) matrices, the second origin/destination (O/D) matrices. The
bridge between these matrices would be provided by a logistics module. The first practical
result of this recommendation was a logistics model for the Trans-Pennine corridor,
presented recently at the European Transport Conference (Jin et al, 2005).

Freight trips and networks
At the national level, Belgium (Beuthe and Jourquin), the Netherlands (Tavasszy), the UK
(DfT), Finland (Florian) and Sweden (Swahn) have developed hypernetwork approaches
for freight network modelling. These network assignment models simultaneously treat
mode and route choice; the Dutch model includes choice of cehicle type as well. Beside
the Belgian model, there are at least 2 other models (STEMM and SCENES) that use a
multimodal network assignment approach. These models work largely on aggregate data.
Other countries usually treat mode choice and route choice separately. At the basis of
mode choice models lie RP and SP datasets. Recent SP or combined RP/SP work for
freight mode choice was carried out in Italy (Danielis), the UK (Shinghal and Fowkes,
2001) and the Netherlands (de Jong). Network assignment has received relatively little
attention, although MUC assignment for road networks is becoming increasingly
important, while truck shares on the road are growing. MUC assignment routines for
freight were developed by Bliemer (2004) for road and by Lindveld (2003) for inland
waterways.
The link between mode and route choice is a weak one. The usual approach uses fixed
conversion factors from tonnes to vehicles, loading units, ships or wagons, for each mode
of transport and occasionally differentiated by sector or commodity group. Although there
is some literature available that links shipment size and mode choice (Mc Fadden,
Abdelwahab & Sargious), even once shipment sizes and modes are known, it is difficult to
develop models due to data difficulties. Especially as both services and product sectors
generate freight movements, and as vans carry both passengers and freight, empirical
challenges are great. Another problematic area is the difficulty to model empty trips, when
it is difficult to observe empty trips. A practical insight is given by Holguín-Veras and
Thorson (2003) on this matter. Wigan et al. (2002) discuss the challenges in the broader
area of modelling commercial, service and light goods movements.

8

As to the general state of the art in urban goods modelling, we can say that presently,
local freight models are not much different from regional or global ones. Taniguchi et al.
(2002) presents an overview of available models in “Modelling city logistics”. City logistics
models involve either prescriptive/normative) approaches (for single firms, or groups
operating as one) or descriptive approaches, where the latter do not take into account the
logistics processes behind freight traffic. Mostly the techniques operated in descriptive
models are direct demand models, which do not take into account explicitly the choice of
mode or vehicle type. Some recent new work in freight trip generation which takes into
account various vehicle types was presented by Iding et al (2003) and Steinmeyer (2005).
Especially at the urban level, hardly any transport statistics are available to help with
developing freight transport demand models. Where firm level data are available,
interesting possibilities open up including detailed microsimulation (see e.g. Barcelo,
2006). Groothedde (2005) presents a simulation approach where use is made of a mix of
public and private data, to develop a detailed spatial database of consumer goods
movements, for purposes of microsimulation of logistics chains.

5. Concluding remarks
The aim of this paper was to describe the major lines of freight demand model
development that have developed outside the USA. We have provided an overview of the
key policy issues and the associated modelling needs. We have identified 3 major lines of
model development, and introduced the state of the art in these areas.
Looking back at the list of policy issues presented in Chapter 2, our overall conclusion is
that a number of areas are still not covered sufficiently. In particular we lack sufficient
knowledge at the network level of the many asymmetric interactions between freight and
passenger traffic. Concerning the 3 lines of development highighted in this paper, it is clear
that this is work in progress, despite the fact that the main bottlenecks for their
introduction, as well as the early adopters can already be identified.
A common thread through all 3 areas of innovation is the challenge to create new data
about freight flows that have not been available in that form before. The availability of
advanced techniques for data gathering will influence our modelling abilities in the future.
New observation methods such as cameras and radar will allow a continuous monitoring
of freight flows. Also, new regulations concerning freight security build up a great
administrative account of freight passing certain checkpoints. Until these sources become
available, however, a certain amount of creativity is needed in combining aggregate and
disaggregat datasources.

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9

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