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.

Highlights

  • Freight transport modelling is shifting from an aggregated perspective towards a behavioural and disaggregate one (Tavasszy and de Jong 2014)

  • Unlike earlier discrete-continuous models in freight transport we model a situation where multiple alternatives can be used at the same time, the model is directly based on micro-economic theory and simultaneous estimation is used

  • Freight price, travel time and headway have a negative sign and Free Alongside Ship (FAS) price had a positive influence in the utility function

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Summary

Introduction

Freight transport modelling is shifting from an aggregated perspective towards a behavioural and disaggregate one (Tavasszy and de Jong 2014). Except for a few very recent studies (for example Khan and Machemehl (2017a, b) and Rashidi and Roorda (2018), all of them in urban context) choices in the freight context have been treated as mutually exclusive alternatives within the traditional discrete framework (Chow et al 2010; Danielis and Marcucci 2007; de Jong et al 2013, 2000, 2014; Rich et al 2009; Vellay and de Jong 2003). There have been some efforts to model these situations in the past, starting with applications in passenger transport, electricity demand or consumer preferences (Dubin and McFadden 1984; Hanemann 1984; de Jong 1990; Kim et al 2002)

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