Exploring Multiple‐discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina

Journal Article (2021)
Author(s)

Rodrigo Javier Tapia (TU Delft - Transport and Planning, Universidade Federal do Rio Grande do Sul)

Gerard Jong (University of Leeds)

Ana M. Larranaga (Universidade Federal do Rio Grande do Sul)

Helena B. Bettella Cybis (Universidade Federal do Rio Grande do Sul)

Transport and Planning
Copyright
© 2021 Rodrigo Javier Tapia, Gerard de Jong, Ana M. Larranaga, Helena B. Bettella Cybis
DOI related publication
https://doi.org/10.1007/s11067-021-09531-y
More Info
expand_more
Publication Year
2021
Language
English
Copyright
© 2021 Rodrigo Javier Tapia, Gerard de Jong, Ana M. Larranaga, Helena B. Bettella Cybis
Transport and Planning
Issue number
3
Volume number
21
Pages (from-to)
581-608
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

There are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against.