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

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...

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Bibliographic Details
Published inNetworks and spatial economics Vol. 21; no. 3; pp. 581 - 608
Main Authors Tapia, Rodrigo J., de Jong, Gerard, Larranaga, Ana M., Bettella Cybis, Helena B.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.09.2021
Springer Nature B.V
Subjects
Civil Engineering
Consolidation
Economic activity
Economics
Economics and Finance
Extreme values
Freight transportation
Heterogeneity
Lead time
Operations Research/Decision Theory
Regional/Spatial Science
Travel time
Utility functions
Argentina
Willingness to pay
Freight modelling
Multiple Discrete Extreme Value Model
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Summary: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.
ISSN:1566-113X
1572-9427
DOI:10.1007/s11067-021-09531-y