Measuring Freight Transport Elasticities with a Multimodal Network Model

Measuring Freight Transport Elasticities with a Multimodal Network Model


Michel Beuthe, UCLouvain-Mons, Bart Jourquin, UCLouvain-Mons, Natalie Urbain, UCLouvain-Mons


The paper presents estimates of direct and cross cost elasticities of transport freight demand for the three inland modes: road, rail and inland waterways. These are based on a multimodal transport modeling of the Rhine freight market, developped in the context of the European project ECCONET. Comparisons and interpretations are made of the various estimates already available in the literature.


Measuring freight transport elasticities with a multimodal network model
M. Beuthe, B. Jourquin and N. Urbain
Group transport and Mobility UCLouvain - Mons, 151 Ch. de Binche, B-7000, Mons, Belgium
There are not many analyses of freight transport elasticities to be found in the literature, and available estimates cover a wide range of values according to modes and cases. Such a diversity of results could be expected since similar results can be observed in the numerous studies on passenger transport demand elasticities. Indeed, the type of modeling, the differences between transport markets, data set and spatial scope may substantially affect the estimates, which may also correspond to short or long run elasticities. These differences must be taken into account for the interpretation of the results and their use in formulating transport policies.
Our work within the European ECCONET project on climate impacts on inland navigation led us to set up a full multimodal freight transport model over the Rhine area market (Beuthe et al., EWGT 2012, Procedia 2012), from which it is possible to extract transport demand elasticities. Earlier, we applied a rather similar approach using the multimodal freight model NODUS calibrated on Belgian transport data (Beuthe et al., TR-E, 2001). Given the high density of the Belgian network and its fine zoning mesh, a rather simple ‘all-or-nothing’ algorithm was then applied, which assigned each origin-destination traffic to a unique solution between all available modes, routes and means solutions. The aggregate direct cost elasticities in respect of tonnage were of the same order of magnitude as those available in the literature at that time: -0.59 for road, -1.77 for rail and -2.13 for waterways. In respect of tons-km, rail and waterways demands were still elastic, though at a smaller level, and so were road elasticities at -1.21.
In the present research, the zoning over the relevant continental Europe’s regions is at the much larger NUTS 2 level, and a ‘multi-flows’ assignment, available in more recent versions of NODUS, was applied, which spreads traffic between each origin and destination over several possible solutions. The outcomes of this modeling generally show inelastic demand reactions to cost variations, mostly within the range -0.4 to -0.7 like in the European meta-model by de Jong et al. (Transport Policy, 2004). They are stronger than the recent estimates by Rich et al. (J. of Transport Geography, 2011) which were within the range -0.1 to -0.4, on the basis of a Scandinavian freight demand model on a network with sparser multimodal connections.
The paper first reviews the state of knowledge in the field, the various results available, their modeling and data differences. Next, it explains the multimodal NODUS model specificities as applied on the Rhine area market. Afterwards, it presents a full set of direct and cross-elasticities, both in respect of tonnage and tons-km, for 11 categories of commodities, including the containers. These results are commented and their coherences discussed. The final section attempts to draw some synthetic conclusions and recommendations on the good use of the alternative results in different circumstances.


Association for European Transport