Accommodating Risk Attitudes in Freight Transport Behaviour Research

Behavioural choice modelling is growing in interest as a framework to understand the decision-making of shippers, freight forwarders and other freight agents. Since the 1980s, we have witnessed an increasing number of freight behaviour studies, primarily the freight mode choice, where the roles of one or more freight decision-makers has been addressed, which was neglected in the traditional aggregate approach to freight demand modelling. Stated preference techniques have become a main approach to establishing the role of attributes that define the key drivers in the freight distribution chain. The underlying theory of choice is based on the neoclassical economic assumption that a decision-maker, in choosing, acts as if they are a utility maximizer (working to deliver a profit maximization or cost minimization outcome for the freight business), and this utility maximizing behaviour engenders a population theory of Random Utility Maximization (RUM). Despite the continuing appeal of the RUM framework in applied travel choice studies, a number of specific application assumptions have been questioned by studies in psychology and behavioural economics, arguing that the decisions made by agents are often conditioned on a number of underlying psychological components, one of which is risk attitude. This paper reviews recent freight behaviour studies established on RUM, and presents a major limitation of adopting a risk-neutral assumption through its linear utility specification. Using an existing freight stated choice data set, a nonlinear utility model is estimated which reveals risk-taking attitudes of transporters and shippers. An alternative behavioural paradigm, Rank-Dependent Utility Theory (RDUT), is introduced and incorporated to better accommodate trip time variability, a feature of growing importance in transport systems (passenger and freight). The proposed attribute-specific extended RDUT framework that accommodates the attitude towards risk and preference in freight transport behaviour modelling requires new data, which we detail.