Modeling heterogeneous risk-taking behavior in route choice: A stochastic dominance approach

Xing Wu, Yu Nie*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

This paper proposes a unified approach to modeling heterogonous risk-taking behavior in route choice based on the theory of stochastic dominance (SD). Specifically, the first-, second-, and third-order stochastic dominance (FSD, SSD, TSD) are respectively linked to insatiability, risk-aversion and ruin-aversion within the framework of utility maximization. The paths that may be selected by travelers of different risk-taking preferences can be obtained from the corresponding SD-admissible paths, which can be generated using general dynamic programming. This paper also analyzes the relationship between the SD-based approach and other route choice models that consider risk-taking behavior. These route choice models employ a variety of reliability indexes, which often make the problem of finding optimal paths intractable. We show that the optimal paths with respect to these reliability indexes often belong to one of the three SD-admissible path sets. This finding offers not only an interpretation of risk-taking behavior consistent with the SD theory for these route choice models, but also a unified and computationally viable solution approach through SD-admissible path sets, which are usually small and can be generated without having to enumerate all paths. A generic label-correcting algorithm is proposed to generate FSD-, SSD-, and TSD-admissible paths, and numerical experiments are conducted to test the algorithm and to verify the analytical results.

Original languageEnglish (US)
Pages (from-to)382-404
Number of pages23
JournalProcedia - Social and Behavioral Sciences
Volume17
DOIs
StatePublished - 2011

Keywords

  • General dynamic programming
  • Risk aversion
  • Route choice
  • Ruin aversion
  • Stochastic dominance

ASJC Scopus subject areas

  • Social Sciences(all)
  • Psychology(all)

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