The estimation and application of a freight forecast model for choice of mode and crossing is described. The model is estimated as an aggregate weighted discrete choice model, in which origin-destination (OD) matrices andlevel-of-service (LOS) matrices are applied directly to specify utility functions for the different choice alternatives. The model is estimated andapplied for 13 commodity groups with very different mode choice patterns,spanning from strict bulk commodities to high-value commodities. In the model, utility functions are estimated only from the mode choice, which consist of five modes; Truck, Combi-Rail, Combi-Ship, Rail, and Ship. The traffic loads on crossings are calibrated subsequently so that the model replicates base matrices according to modal split and the crossing pattern. The zone structure of the model covers most of Europe, although the geographical focal point of the model is the Oeresund region. The model is validated statistically by reviewing the elasticity structure and the value of time across commodity groups and modes. It was then used to simulate a sequence of reference scenarios in order to test its applicability. The model has been specified in a log-space formulation, e.g. LOS variables related to time as well as cost are transformed on a logarithmic scale. There have been two major reasons for this. Firstly, the log-space has generally performed better in terms of goodness-of-fit. Secondly, by the log-space formulation, scale-dependency in the derived demand responses is avoided. In the log-space, a direct proportional relationship is obtained between demandand supply. For most commodity groups, the bulk modes (rail and ship) arevery different from the other modes: The cost is generally much lower, the travel time much higher, and so are the volumes on particular links. Consequently, it has been difficult to estimate the effect of different time components (e.g. short vs. long trips and waiting time vs. in-vehicle time) in a full joint estimation with all modes included. The problem is that for non-bulk modes, time is decomposed into vehicle time and waiting time,whereas for bulk modes there is only a single time and cost component. Itmeans that estimating time and cost for all modes will seriously affect the scaling between, e.g. waiting time and in-vehicle time. A solution to this problem would be to estimate an individual scaling of time for bulk modes, however, this is not possible due to strong correlations between LOS variables. Another way to overcome (a) the scaling and (b) the correlationproblem is to implement a two-stage estimation procedure. In the first stage only non-bulk modes are considered. This enables the estimation of thedifferent time components for truck and combi modes. In the second stage,the scaling of the different time components from the first stage is fixed. The purpose is to estimate an overall scaling of time and cost. Due to the two-stage estimation, it was possible to overcome correlation problemsand to estimate different time components, e.g. waiting time, in-vehicle time and divided into different distance bands. Generally, the results of the model considering demand sensitivity and the value of time, conforms well to the reference literature, and suggest that aggregate choice estimation is an option when disaggregate data is not available. For the coveringabstract see ITRD E145999
Abstract