Elsevier

Fuzzy Sets and Systems

Volume 116, Issue 1, 16 November 2000, Pages 77-101
Fuzzy Sets and Systems

Fuzzy route choice model for traffic assignment

https://doi.org/10.1016/S0165-0114(99)00039-1Get rights and content

Abstract

In order to represent traffic assignment on a road network, we propose a new route choice model taking account of the imprecisions and the uncertainties lying in the dynamic choice process. This model makes possible a more accurate description of the process than those (deterministic or stochastic) used in the literature. We assume that drivers choose a path all the more than it is foreseen to have a lesser cost. The predicted cost for each path is modelled by a fuzzy subset which can represent imprecision on network knowledge (e.g. length of links) as well as uncertainty on traffic conditions (e.g. congested or uncongested network, incident ). The costs of all possible paths are compared and result in an attractiveness degree for each path. Various comparison indices can be used to represent the different possible natures of drivers when making their decisions (pessimistic/optimistic, risk-taking/risk-averting). The fuzzy choice model was compared with the LOGIT model (a widely used stochastic discrete choice model) and has been proved to be able to find the same results. The effect of Advanced Travellers Information Systems (ATIS) on drivers is modeled as a modification of the imprecision or the uncertainty of the predicted cost of a route.

References (40)

  • J.F. Baldwin et al.

    Comparison of fuzzy sets on the same decision space

    Fuzzy Sets and Systems

    (1979)
  • C. Buisson, J.-P. Lebacque, J.-B. Lesort, Strada, a discretized macroscopic model of vehicular traffic flow in complex...
  • G.E. Cantarella et al.

    Dynamic processes and equilibrium in transportation networkstowards a unifying theory

    Transportation Sci.

    (1995)
  • CONTRAM-I: Modelling the effects of incidents in urban networks, Final Report by the TRG to TRL, University of...
  • R. Dial

    A probabilistic multipath traffic assignment model which obviates path enumeration

    Transportation Res.

    (1971)
  • E.W. Dijkstra

    A note on two problems in connection with graphs

    Numer. Math.

    (1959)
  • D. Dubois et al.

    Algorithmes de plus court chemin pour traiter des donnés floues

    RAIRO Rech. Opér.

    (1978)
  • D. Dubois et al.

    Operations on fuzzy numbers

    Internat. J. Systems Sci.

    (1978)
  • D. Dubois et al.

    Théorie des possibilités, application à la représentation des connaissances

    (1987)
  • D. Dubois, H. Prade, R. Sabbadin, Logique possibiliste et décision qualitative, in Rencontres francophones sur la...
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    Expanded version of a talk presented at the 9th mini Euro conference Fuzzy Sets in Traffic and Transport Systems held in Budva (Yugoslavia), September 15–19, 1997.

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