Drivers may be influenced by some factors, such as the front vehicle's velocity and headway; but, the influences of vehicle honk on traffic flow and driver behavior have seldom been investigated. The present study took this issue into account by proposing a new symmetric two-lane cellular automaton model. It is based on the symmetric two-lane cellular automata (STCA) model that was proposed by Chowdhury. The lane-changing rules of STCA were revised, and some other rules were added that took the honk effect into account. This study also focused on the relationship between drivers' characteristic and honk actions. The results showed that different kinds of drivers made different decisions when their front vehicle hindered him. Based on the observation, the added cellular automaton rules included not only the honk rules but also the driver's characteristics. Finally, some simulations were made. The results showed that the honk action enhanced the flux in the intermediate-density range. Besides, the relation between the percentage of three type of driver and traffic flux was considered. The results figured out that the rash driver's honk increased the lane changing times, which only resulted in the fluctuation of traffic flows. So with the increasing of rash driver's percentage, the traffic flow flux did not increase in the high-density range. As far as the percentage of cautious driver, the traffic flow had less fluctuation than the former results. In summary, it can be argued that the honk behavior is not encouraged in the asymmetric two-lane model.


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    Titel :

    Honk Effect on Two-Lane Traffic Flow Considering Driver Behaviors


    Beteiligte:
    Xu, Kun (Autor:in) / Ruan, Qian (Autor:in) / Luo, Huachun (Autor:in)

    Kongress:

    Second International Conference on Transportation Information and Safety ; 2013 ; Wuhan, China


    Erschienen in:

    ICTIS 2013 ; 1566-1571


    Erscheinungsdatum :

    2013-06-11




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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