Abstract
Freight transportation plays an increasingly important role in sustainable development. However, freight travel demand has not been understood comprehensively, due to its unique features: freight activities are the result of collaboration among freight agents. It distinguishes freight transportation from passenger transportation, in which travel decisions are made mostly by individuals. Specifically, two processes in the collaboration can be observed: partner selection and joint decision making. Using the supplier-customer collaboration as an example, partner selection is a process for suppliers and customers to evaluate their potential partners and select the best one. Joint decision making allows suppliers and customers to seek common interests and make compromises. As a traditional travel demand model cannot model the two processes effectively, this research develops an innovative econometric model, spatial matching model, to bridge the gap. The proposed model is specified based on freight agents’ behavioral, estimated by Bayesian MCMC methods, and demonstrated by numerical examples. The proposed model and estimation methods can recover the coefficient values in the econometric models, and establish the relationship between the influential factors and the observed matching behavior. The analysis improves the understanding of freight travel demand in a behavioral-consistent manner and enriches the body of freight demand modeling literature.
Highlights
An efficient freight system can transport an extraordinarily large number of products for daily life, stimulates demand for goods, and employs millions of people
This paper proposes an extension to the model that can further improve the understandings of freight behavior: (1) the spatial interaction between freight agents can be characterized; (2) the matching structure is many-to-many, in comparison to the one-to-many matching structure in the literature; and (3) the joint decision-making outcome is ordinal, in comparison to the binary outcome of firm’s initial public offering (IPO)
This paper develops an econometric model, the spatial matching model, to characterize freight travel demand
Summary
An efficient freight system can transport an extraordinarily large number of products for daily life, stimulates demand for goods, and employs millions of people. In the joint decision making process, discrete outcome models can be estimated with independent variables of characteristics of each agent (e.g., supplier’s size and customer’s industry sector) and joint factors (e.g., distance between two agents) Such two equations can be connected by specifying a correlation in the error term, characterizing the sample selection feature: the joint decisions can only be made by matched agents. This paper proposes an extension to the model that can further improve the understandings of freight behavior: (1) the spatial interaction between freight agents can be characterized; (2) the matching structure is many-to-many, in comparison to the one-to-many matching structure in the literature; and (3) the joint decision-making outcome is ordinal (e.g., shipping frequency), in comparison to the binary outcome of firm’s IPO. The result of numerical examples is discussed and followed by conclusions
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