Travelers always perform some preference during the decision-making process. The preference will affect the decision results and can be improved by continuously learning. In order to understand the influence of individual preference on travel behavior choice , two individual preferences, including indifference preference and compulsive preference are considered in the paper. Two updating mechanisms of compulsive preference are proposed to obtain the choosing probability of all alternatives. Reinforcement learning models are established integrating the gain stimulating and loss stimulating considering expected utility. Nguyen Dupuis network is adopted for numerical simulation to study the updating process. Simulation results denote that the equilibrium state is much more efficient when preference learning mechanism is considered comparing with the traditional stochastic user equilibrium model, and can decrease the total travel time greatly, which can be applied for urban traffic management. Personalized traffic guidance is the effective solution to traffic congestion in the future


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    Titel :

    Reinforcement learning of route choice considering traveler’s preference


    Weitere Titelangaben:

    X. LONG ET AL.
    TRANSPORTATION LETTERS


    Beteiligte:
    Long, Xueqin (Autor:in) / Mao, Jianxu (Autor:in) / Qiao, Zhongbao (Autor:in) / Li, Peng (Autor:in) / He, Wei (Autor:in)

    Erschienen in:

    Transportation Letters ; 16 , 7 ; 658-671


    Erscheinungsdatum :

    08.08.2024


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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