As the key nodes of urban road network, intersections are the most complex and hazardous areas. Self-explaining intersections may improve traffic safety by assisting drivers’ performances and reducing their failures. A simulator-based experiment with 10 designed scenes in different intersections was conducted to collect drivers’ behavioral data of 20 participants. Questionnaire surveys were developed to assess the drivers’ driving load. Driving performances under different intersections were analyzed including the speed choice, drivers’ visual characteristics, and the homogeneity of driving trajectories. The results show characteristics of intersections based on self-explaining road theory. The countermeasures to improve fault tolerance and safety of intersections were proposed.


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    Titel :

    Analyses on Drivers’ Performance at Urban Road Intersections Based on Self-Explaining Roads


    Beteiligte:
    Yu, Wenlin (Autor:in) / Jiang, Xiaobei (Autor:in) / Li, Ruihao (Autor:in) / Cheng, Qian (Autor:in) / Guo, Jiawen (Autor:in) / Wang, Wuhong (Autor:in)

    Kongress:

    International Conference on Transportation and Development 2020 ; 2020 ; Seattle, Washington (Conference Cancelled)



    Erscheinungsdatum :

    31.08.2020




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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