Bounded Rationality in Hyperpath Assignment: The Locally Rational Traveler Model

The concepts of optimal strategy and hyperpath were born under the paradigms of equilibrium and expected utility theory, which assume perfect rationality. The initial scope for these concepts was frequency-based transit assignment, but increasingly hyperpaths are used also for other transport problems, such as scheduled-based transit assignment and vehicle routing. Extensions to the original formulation have been developed to take into account congestion, the availability of information, and time-dependent networks. Psychological research has shown that human rationality is bounded by limitations on computational ability. Frequently the issue has been neglected assuming that travelers can learn from experience but recent literature shows that this cannot be taken for granted. In particular, humans can evaluate only a reduced number of alternatives, so hypothesizing that travelers are able to think in terms of hyperpaths whatever their length can give rise to unrealistic assignments. This paper presents a behavioral model in which “myopic” travelers can determine hyperpaths only if their length is under a certain threshold. At each node they reroute choosing stochastically a local intermediate node according to the expected cost of the hyperpath to this local destination and an a priori estimate of the cost of the remaining trip to the final destination. Implementation on a test network demonstrates that the model can generate results that differ considerably from those derived from the perfect rationality assumption, and so further work is needed to improve its realism and to test it against real world problems.


  • English

Media Info

  • Media Type: DVD
  • Features: Figures; References; Tables;
  • Pagination: 15p
  • Monograph Title: TRB 89th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01155607
  • Record Type: Publication
  • Report/Paper Numbers: 10-0100
  • Files: TRIS, TRB
  • Created Date: Jan 25 2010 10:07AM