智能网联环境下CAV混行车流集聚策略及分析

Agglomeration strategy and analysis of CAV mixed traffic flow in intelligent and connected environment

针对目前的交叉口控制方法无法适应于网联人工驾驶车辆(CHV)与网联自动驾驶车辆(CAV)的混行而导致冲突增多、效率下降的问题,提出了一种适用于主干路交叉口的基于多智能体系统(MAS)的CAV混行车流集聚控制模型(MTF-ACM).构建基于V2V(车对车)技术的混行车流集聚策略,以降低混行车流的随机性.设计虚拟动态预信号,通过时空同步机制对集聚车队进行速度诱导.根据主预信号协同配时策略,使集聚车队以最大可能不停车通过交叉口.研究结果表明:当CAV的市场渗透率(MPR)为60%时MTF-ACM取得通行能力的最佳效益;当交通流量临近饱和状态时,相较于无ACM和CAV-ACM,MTF-ACM平均延误时间和停车次数分别降低30%和50%以上,燃油消耗和CO_(2)排放分别减少20.59%和22.21%.

A multi-agent system(MAS)based connected autonomous vehicle(CAV)mixed traffic flow aggregation control model(MTF-ACM)was proposed to address the issue of increased conflicts and reduced efficiency caused by the inability of current intersection control methods to adapt to the mixed traffic of connected manual driving vehicles(CHV)and connected autonomous driving vehicles(CAV).A vehicle-to-vehicle(V2V)based mixed traffic flow aggregation strategy was constructed.To reduce the randomness of mixed traffic flow,a virtual dynamic pre-signal was designed,and the speed of the agglomerated platoon was induced through a spatiotemporal synchronization mechanism.According to the collaborative timing strategy of the main pre-signal,the agglomerated platoon passes through the intersection with the maximum possibility of not stopping.The research results showed that when the market penetration rate(MPR)of CAV is 60%,MTF-ACM achieves the best benefit of capacity,and when the traffic flow is approaching saturation.When the traffic flow approaches saturation,compared to no ACM and CAV-ACM,the average delay time and parking frequency of MTF-ACM decrease by more than 30%and 50%,while fuel consumption and CO_(2) emissions decrease by 20.59%and 22.21%,respectively.