Risk Route Choice Analysis and the Equilibrium Model Under Anticipated Regret Theory

The assumption about travelers’ choice behavior has major influence on traffic equilibrium analysis. Previous researches about the travelers’ route choice were mainly based on the expected utility maximization theory. While, as the gradually increasing knowledge about the uncertainty of the transportation system, researchers have realized that the expected utility maximization theory has much constraint, because expected utility maximization requires that travelers must be “absolute rational”, but in fact, travelers are not truly “absolute rational”. The anticipated regret theory proposes an alternative framework to the traditional risk-taking in route choice behavior which might be more scientific. The authors apply the anticipated regret theory to the analysis of the risk route choosing process, and construct an anticipated regret utility function. By a simple case which includes two parallel routes, the authors analyze the route choosing results influenced by the risk aversion degree, regret degree and the environment risk degree. Moreover, the authors establish the anticipated regret theory based user equilibrium model. The equivalence and the uniqueness of solution are proved, and an algorithm is proposed to solve the model. Both the model and the algorithm are demonstrated in a real network. By an experiment, we compare the model results and the real data. The authors find the model results can be similar to the real data if we choice a proper regret degree parameter. This illustrates that the model can explain the risk route choosing behavior better. Not only that, the authors also find the travelers’ regret degree is increased when the environment become more and more risk.


  • English

Media Info

  • Media Type: Print
  • Features: Figures; References; Tables;
  • Pagination: pp 33-43
  • Serial:
  • Publication flags:

    Open Access (libre)

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Filing Info

  • Accession Number: 01523092
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 2 2014 2:50PM