A Route Choice Model Based on Cellular Automaton and Bounded Rationality: Empirical Analysis of Transportation Network in Sichuan-Tibet Region

In order to study the influence of travelers’ self-adaptive adjustment behavior on transportation network under the assumption of bounded rationality, a travel route choice model with individual interactive mechanism is established by using cumulative prospect theory and cellular automaton mode. In the model, travelers are divided into risk-seeking type and risk-averse-type, taking the generalized travel cost as the reference point, the choice rules of heterogeneous reference points are proposed in the study, and the evolution rules of dynamic reference points with heterogeneous characteristics are designed based on the idea of cellular genetic algorithm, so that travelers can dynamically adjust their generalized travel cost budget according to the changes of decision environment. Finally, the improved method of successive average algorithm is designed to solve the network assignment based on bounded rationality. The authors take Sichuan-Tibet region as the authors' study object, mainly focus on the multi-modal transportation network in that region, and apply model and algorithm proposed in this study into travel route choice analysis and traffic assignment. It is found that the cellular automaton model can simulate the dynamic change process of the travel reference point very well, and the route choice model established by cumulative prospect theory and cellular automaton has practical significance. Such a conclusion is obtained from the empirical analysis. The authors find that the types of risk attitude of travelers under the bounded rational behavior will greatly affect the results of traffic assignment.

Language

  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: 20p

Subject/Index Terms

Filing Info

  • Accession Number: 01764058
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-00024
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:18AM