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Freight modelling: an overview of international
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Freight Modelling – An overview of international
experiences
Lóránt A. Tavasszy
1Paper 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
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
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
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 Summary of modelling challenges and techniques
Decision problem Typical modelling challenges Typical techniques employed
Production and
considerations logistics choice models (‘90’s) Transportation services choice of mode
intermodal transport Network and routing routing and congestion
tour planning
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 benefit-cost 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
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).
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.
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.
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.
6.
References
Abdelwahab, W. and Sargious, M. (1992) Modelling the demand for freight transport, Journal of Transport Economics and Policy, January, pp. 49-70.
Bergman, T. (1987)., New generations of freight models: more logistically oriented models, need and possibilities, Paper presented at the International Meeting on Freight, Logistics and Information Technology, The Hague, 17 and 18 December 1987.
Beuthe, M., Jourguin, B., Geerts, J. F. and Koul A Ndjang' HA, C. (2001) Freight transportation demand elasticities: a geographic multimodal transportation network analysis, Transportation Research E, 37,pp. 253-266.
Bliemer M.C.J.; Bovy P.H.L (2003), Quasi-variational inequality formulation of the multiclass dynamic traffic assignment problem, Transportation Research Part B, Vol. 37, No 6, pp. 501-519
Boerkamps, J. and A. van Binsbergen (1999), GoodTrip - A New Approach for Modelling and Evaluation of Urban Goods Distribution Urban Transport Systems, 2nd KFB-Research Conference Lund, 1999
Bröcker, J. (1998) Operational spatial computable general equilibrium models, Annals of Regional Science, 32, pp. 367-387.
Bröcker, J., Kancs, A., Schürmann, C., Wegener, M. (2001). Methodology for the Assessment of Spatial Economic Impacts of Transport Projects and Policies. IASON Deliverable 2. Kiel/Dortmund: Christian-Albrechts-Universität Kiel/Institut für Raumplanung, Universität Dortmund.
Burgess, A. (2001) The European transport model directory (MDir), description of modal split modeling in European transport models on the basis of Mdir. Paper presented at the THINK-UP Workshop 9, Rotterdam.
Caspersen, S., L. Eriksen and M. Marott Larsen (2000). The BROBISSE model – a spatial general equilibrium model to evaluate the Great Belt link in Denmark. AFK, Institute of Local Government Studies, Copenhagen
Chisholm, O’ Sullivan (1973), Freight flows and spatial aspects of the British economy, Cambridge, The University Press.
Crainic, T. G., Florian, M., Guelat, J. and Spiess, H. (1990, Strategic planning of freight
transportation: STAN, an interactive-graphical system. Transportation Research Record, 1283, pp. 97-124.
Danielis, R. (2002), Freight Transport demand and stated preference experiments (partly in Italian), FrancoAngeli, Italy
Fischer, M., M.L. Outwater, L. Luke Cheng, D.N. Ahanotu, R. Calix (2005), An Innovative Framework for Modeling Freight Transportation in Los Angeles County, Transportation Research Record No. 1906
Shinghal, N., Fowkes, A.S. (2002). Freight Mode Choice and Adaptive Stated Preferences, Transportation Research E, Logistics and Transportation Review, (38), pp.367-378
Gédéon, C., M. Florian, T.G. Crainic (1993), Determining origin-destination matrices and optimal multi-product flows for freight transportation over multimodal networks, Trans. Res.B. Vol.27B, No.5, Pp.351-368
Groothedde, B. (2005), Collaborative Logistics and Transportation Networks, PhD Dissertation Delft University of Technology, TRAIL Research School, Delft
Harker, P. & T. Friesz. (1986) “Prediction of intercity freight flows II: Mathematical formulations.” Transportation Research, 20(B), 155-174.
Holguín-Veras, J. and E. Thorson (2003), Practical implications of Modeling Commercial Vehicle Empty Trips, Journal of the Transportation Research Record No. 1833, Transportation Research Board of the National Academies, Washington, D.C., pp. 87-94
Hussain, I. and L. Westin (1997). Network Benefits from Transport Investments under Increasing Returns to Scale: A SCGE Analysis. Umeå Economic Studies, Paper no. 432. Centre for Regional Science (CERUM), Umeå.
Ivanova, O., A. Vold and V. Jean-Hansen (2002). PINGO a Model for Prediction of Regional and Interregional freight transport. TØI report 578/2002. Institute of Transport Economics, Oslo.
Jin, Y., I. Williams and M. Shahkarami, Integrated regional economic and freight logistics modelling: Results from a model for Trans-Pennine corridor, paper presented at the 2005 European Transport Conference Strasbourg, Association for European Transport, London, 2005
Jong, de (2004), National and International Freight Transport Models: An Overview and Ideas for Future Development, Transport Reviews, Vol. 24, No. 1, 103-124
Jong, de, G., M. Ben-Akiva, M. Florian, S.E.Grønland, M.van der Voort, Specification of a Logistics Model for Norway and Sweden, paper presented at the 2005 European Transport Conference Strasbourg, Association for European Transport, London, 2005
Knaap, T. and J. Oosterhaven (2000). The welfare effects of new infrastructure: An economic geography approach to evaluating a new Dutch railway link. Paper presented at the North American RSAI meeting, Chicago, November 9-12.
Koike, A., T. Ueda and M. Miyashita (2000). Spatial Computable General Equilibrium Model for passenger transport improvement: Evaluation of Japanese New Shinkansen Project. Presented at the World Conference of Regional Science Association International, Lugano, May, 2000.
Lakshmanan, T.R. and W.P. Anderson (2002). Transportation Infrastructure, Freight Services Sector and Economic Growth. A White Paper prepared for The U.S. Department of Transportation Federal Highway Administration. Center for Transportation Studies, Boston University.
Lindveld, C., S.Catalano, K. Carlier, P.H.L. Bovy (2003), Supernetwork Approach Toward Multimodal Route Choice Modeling, Paper 03-3951, Proceedings of the 82nd Annual Meeting of
theTransportation Research Board, Washington, DC
Löfgren, H. and S. Robinson (1999). Spatial Networks in Multi-region Computable General Equilibrium Models. TMD Discussion Paper No. 35. International Food Policy Research Institute (IFPRI), Washington D.C.
Miyagi, T. (2001). Economic appraisal for multiregional impacts by a large scale expressway project: a spatial computable general equilibrium approach. Tinbergen Institute Discussion Paper: TI-2001 066/3. . http://www.tinbergen.nl/home.html
Nordman, N. (1998). Increasing returns to scale and benefits to traffic: A Spatial General Equilibrium Analysis in the case of two primary inputs. Centre for Regional Science (CERUM), Umeå.
Oosterhaven, J., T. Knaap, C.J. Ruijgrok. and L.A. Tavasszy (2001). On the development of RAEM: the Dutch spatial general equilibrium model and its first application, 41th European Regional Science Association Conference, Zagreb, 2001.
Östlund et al (2003a), Pre-study on modelling local/regional distribution and collection traffic, SIKA - Swedish Institute for Transport and Communications Analysis, Stockholm, Sweden
Östlund et al (2003b), A Logistics module for SAMGODS, SIKA - Swedish Institute for Transport and Communications Analysis, Stockholm, Sweden
Roson, R. (1995). A general equilibrium analysis of the Italian transport system. In Banister, D., Capello, R. and Nijkamp, P., European transport and communications network: Policy evaluation and change. New York: John Wiley & Sons.
SCENES consortium (2000) SCENES European transport forecasting and appended module, technical description; deliverable 4 to EU DGTREN; SCENES Consortium, Cambridge.
Scenes Consortium (2001) SCENES Transport Forecasting Model: Calibration and Forecast Scenario Results. Deliverable 7 to EU DGTREN (Cambridge: SCENES Consortium)
Southworth/Nagurney, Groothedde
Steinmeyer, I., Using National behavioural data on commercial traffic for local and regional applications, Paper #06-1110 presented at the 2006 TRB Conference, Washington DC
Swahn, H., The Swedish National Model system for Goods Transport. A brief introductory overview, SIKA report 2001:
Taniguchi, E, R.G. Thompson (2002), Modeling city logistics, Transportation Research Record 1790, pp. 45-51, National Research Council, Washington, DC
Tavasszy, L.A. (1996), Modelling European Freight Transport Flows, PhD dissertation, Delft University of Technology, Trail Research School, Delft
Tavasszy, L. A., Thissen, M. J. P. M., Muskens, A. C. and Oosterhaven, J. (2002) Pitfalls and solutions in the application of spatial computable general equilibrium models for transport appraisal. Paper presented at the 42nd European Congress of the Regional Science Association, Dortmund. Tavasszy, L. A., van de Vlist, M., Ruijgrok, C. and van de Rest, J. (1998) Scenario-wise analysis of transport and logistic systems with a SMILE. Paper presented at the 8th WCTR Conference, Antwerp.
Tavasszy, L.A., Ruijgrok, C.J. and M.P.J.M Thissen, (2003) Emerging Global Logistics Networks: Implications for Transport Systems and Policies, Urban Growth and Change, Fall Issue, 2003 Thorson, E (2005),The Integrative Freight Market Simulation: An application of experimental economics and algorithmic solutions, PhD Dissertation Rensselaer Polytechnic Institute, NY TRANSFORUM consortium, Deliverable D4.3 (2006), European Transport Modelling & Scenarios: fitness-for purpose analysis, European Commission, Brussels, 2006
Ueda, T., A. Koike and K. Iwakami (2001). Economic Damage Assessment of Catastrophe in High Speed Rail Network. http://www.sse.tottori-u.ac.jp/keikaku_source/berkeley2001/koi2001.pdf Van den Bergh, J.C.J.M., P. Nijkamp and P. Rietveld (eds.) (1996), Recent advances in spatial equilibrium modelling: methodology and applications. Springer-Verlag, Berlin.
Venables, A. and Gasiorek, M. (1998), The Welfare Implications of Transport Improvements in the Presence of Market Failure. Report to the Standing Advisory Committee on Trunk Road Assessment (SACTRA), DETR, London.
Wigan, M.R., F.Southworth (2005), What's Wrong with Freight Models and What Should We Do About It?, Paper prepared for the European Transport Conference, October 2005, Strasbourg, France
Williams, H.C.W.L. (1977). On the formulation of travel demand models and economic evaluation measures of user benefit. Environment and Planning A, vol. 9, pp 284-344.
Williams, I (2003), Feasibility Study of SCGE Models of Goods Flows in Sweden, SIKA - Swedish Institute for Transport and Communications Analysis, Stockholm, Sweden
WSP (2002a). Review of Freight Modelling: Final Report. Prepared for contract no. PPAD 9/134/05 for the ITEA Division of DfT, London 2002
WSP (2002b). Feasibility Study of SCGE Models of Goods Flows in Sweden. Report prepared for the Swedish Institute for Transport and Communications Analysis.
