In the past decades, many improved car-following models based on the full velocity difference (FVD) model have been developed. But these models do not consider the acceleration of leading vehicle. Some of them consider individual anticipation behavior of drivers, but they either do not quantitatively determine the types of driving or artificially divide the driving types rather than deriving them from actual traffic data. In this paper, driver’s driving styles are firstly categorized based on actual traffic data via data mining and clustering algorithm. Secondly, a new car-following model based on FVD model is developed, taking into account individual anticipation effects and the acceleration of leading vehicle. The effect of driving characteristics and leading vehicle’s acceleration on car-following behavior is further analyzed via numerical simulation. The results show that considering the acceleration of preceding vehicle in the model improves the stability of traffic flow and different driving characteristics have different influence on the stability of traffic flow.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A New Car-Following Model considering Driving Characteristics and Preceding Vehicle’s Acceleration


    Beteiligte:
    Yong Zhang (Autor:in) / Ping Ni (Autor:in) / Minwei Li (Autor:in) / Hao Liu (Autor:in) / Baocai Yin (Autor:in)


    Erscheinungsdatum :

    2017




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





    Car Following Control Model Considering Pre-preceding Vehicle Information

    Marumo, Y. / Tanaka, K. / Fukuyama, Y. et al. | British Library Online Contents | 2013



    Acceleration Control Considering Traffic Signals and Motion of Preceding Vehicle for Eco-driving on a Single-lane

    Mukai, Masakazu / Aoki, Hiroshi / Kawabe, Taketoshi | British Library Online Contents | 2017


    Intelligent Vehicle's Driver Model Considering Longitudinal and Lateral Integrated Control

    Sui, Zhen / Liang, Shuo / Tian, Yantao | British Library Conference Proceedings | 2018