Existing learning and adaptive behavior models are typically derived from reinforcement learning from actual experiences in dynamic non-stationary environments. When the dynamics involve uncertainty with respect to the choice behavior of other travelers, travelers might make particular choice options by taking into account their expectations about the behavior of other travelers. Consequently, they will learn not only from their own experiences but also from the extent to which their conjectures about the behavior of other travelers are consistent with actual choices. A general model of interactive learning behavior that evolves toward equilibrium in such strategic situations is described. The properties of the model are examined by using numerical computer simulations. The results of the simulations support the face validity of the formulated model.


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    Title :

    Interactive Learning in Transportation Networks with Uncertainty, Bounded Rationality, and Strategic Choice Behavior


    Subtitle :

    Quantal Response Model


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Han, Qi (author) / Timmermans, Harry (author)


    Publication date :

    2006-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English