As road congestion is exacerbated in most metropolitan areas, many transportation policies and planning strategies try to nudge travelers to switch to other more sustainable modes of transportation. In order to better analyze these strategies, there is a need to accurately model travelers' mode-switching behavior. In this paper, a popular artificial intelligence approach, the decision tree (DT), is used to explore the underlying rules of travelers' switching decisions between two modes under a proposed framework of dynamic mode searching and switching. An effective and practical method for a mode-switching DT induction is proposed. A loss matrix is introduced to handle class imbalance issues. Important factors and their relative importance are analyzed through information gains and feature selections. Household Travel Survey data are used to implement and validate the proposed DT induction method. Through comparison with logit models, the improved prediction ability of the DT models is demonstrated.


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    Title :

    Decision tree method for modeling travel mode switching in a dynamic behavioral process




    Publication date :

    2015




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    Local classification TIB:    770/7000
    BKL:    55.80 / 55.80 Verkehrswesen, Transportwesen: Allgemeines / 74.75 / 74.75 Verkehrsplanung, Verkehrspolitik



    Decision tree method for modeling travel mode switching in a dynamic behavioral process

    Tang, Liang / Xiong, Chenfeng / Zhang, Lei | Taylor & Francis Verlag | 2015




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