Intra-driver heterogeneity is defined as transition of driver’s behavior between usual and unusual, which is an intrinsic feature of drivers while yet to be extensively explored. This study used a large-scale naturalistic driving data set to investigate intra-driver heterogeneity in car-following. We constructed an IDM-based baseline model to represent a driver’s usual behavior; by measuring difference between observed behavior with baseline, unusual behavior was identified. Then, multi-level logit model with random effects was fitted to uncover contributing factors. Among 41 drivers’ 1356 trips, intra-driver heterogeneity was identified in 3194 episodes, which accounts for 15% of the time. Within investigated 24 factors, we found that intra-driver heterogeneity was statistically related with vehicle kinematic features, then traffic flow and surrounding environment, but not driver sociodemographics. Being cut in is the most prominent trigger for intra-driver heterogeneity. These findings garner some remarkable insights into improvement of car-following modeling and many other engineering practices.


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    Titel :

    Investigating the intra-driver heterogeneity in car following behaviour based on large-scale naturalistic driving study


    Beteiligte:
    Rao, Hongyu (Autor:in) / Zhang, Duo (Autor:in) / Qin, Guoyang (Autor:in) / Yue, Lishengsa (Autor:in) / Sun, Jian (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    31.12.2023


    Format / Umfang :

    21 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt






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