With the emergence of intelligent network technology, a mixed traffic flow consisting of connected and automated vehicles (CAVs), manually driven vehicles (MVs) and buses has been formed on the road. Now, how to fully improve the utilization efficiency of dedicated lane and the effective operation of mixed traffic has become a hotspot in current research. Aiming at filling the research gap, this research proposes a traffic strategy for organizing and managing mixed traffic flows in intelligent network environment, the traffic strategy of intermittent bus lane which reasonably arranges the CAVs on the bus lane and ensures the driving priority of buses. In order to simulate the operation mode of mixed traffic flow, it has been designed a mandatory lane-changing rule combined with the discrete CAV and MV car-following model to form cellular automaton model of mixed traffic flow. To further to explore the applicability of the strategy, this research selects the CAV penetration rate, bus departure interval and the number of bus stops as the influencing factors, and carries out simulation evaluation on the operation benefit of the traffic strategy from three aspects: vehicle speed, travel delay, and bus fuel consumption. The simulation results show that after implementing the strategy proposed in this paper, under the conditions of medium and low density, the bus speed increases by 49.3%, and the speed of CAVs increases by 27%. The bus travel time can be saved by up to 14%, the fuel consumption is reduced by 26.3%, the overall traffic flow delay of the road section is reduced by 28.63%, and the total passenger capacity is effectively improved. The traffic strategy intermittent bus lane can give full play to the advantages of bus lane and intelligent network technology, and can effectively improve traffic conditions.


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    Title :

    Research on the Traffic Strategy of Intermittent Bus Lane in Intelligent Network Environment


    Contributors:
    Liu, Yugang (author) / He, Shancheng (author) / Zheng, Shuai (author) / Lin, Yexin (author) / Long, Shiyu (author) / Ge, Leiyu (author)


    Publication date :

    2021-10-22


    Size :

    13900575 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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