LIU Zhuo, LU Kaiming, HE Jia, BI Huibo, CHEN Yanyan, LIU Yiqi. Control Strategy for the Mixed Traffic Flow of CAV and HV in Intersection[J]. Journal of Beijing University of Technology, 2022, 48(6): 608-621. DOI: 10.11936/bjutxb2021100005
    Citation: LIU Zhuo, LU Kaiming, HE Jia, BI Huibo, CHEN Yanyan, LIU Yiqi. Control Strategy for the Mixed Traffic Flow of CAV and HV in Intersection[J]. Journal of Beijing University of Technology, 2022, 48(6): 608-621. DOI: 10.11936/bjutxb2021100005

    Control Strategy for the Mixed Traffic Flow of CAV and HV in Intersection

    • Considering the characteristics of connected autonomous driving vehicle (CAV) and human driving vehicle (HV), a control strategy for mixed traffic flow in intersection was proposed by integrating the passage lock approach with inter-car gap theory. The effectiveness of the proposed strategy was evaluated by a simulation system constructed on SUMO platform. 52 groups of experiments with traffic volume and CAV ratio as input variables under 6 scenarios were implemented. Results show that when CAV penetration is constant, the opportunities available to CAV at the intersection decrease, and the effect of the strategy on reducing delay gradually decreases as the increase of the traffic flow. When the traffic flow is constant, the implementation effect of the strategy is sensitive to the change of CAV permeability. At high penetration, CAV has a greater opportunity to obtain idle temporal and spatial resources, and the reduction effect of average vehicle delay at intersections is more obvious. The proposed strategy can reduce more than 10% of average vehicle delay compared with traditional signal control strategy with low/medium/high CAV penetration under low traffic volume scenario, with medium/high CAV penetration under medium traffic volume scenario, and with high CAV penetration under high traffic volume scenario. The proposed strategy can be used as a supplement to the conventional signal control strategy at intersections under mixed traffic environment, and is also significant to be a reference for intersection organization, optimization and management even under pure intelligent autonomous driving environment.
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