车辆临近交叉口的变道行为会制约交叉口通行效率的提升。基于此，本文提出一种网联车辆环境下城市道路交通流分段协同控制方法(Segmented Cooperative cOntrol Method for Urban Road Traffic Flow，SCOM-URTF)，该方法采用双层优化模型，实现路段功能区动态划分和路段—交叉口交通流的协同优化。上层模型设计了一种分车道速度诱导错位变道策略(Misaligned Lane-changing with Separated Lane Speed Guidance，ML-SLSG)，通过纵向空间错位排列促成左转和右转车辆的快速变道，最小化车辆变道区长度，并均衡车道组交通流量;下层模型以最小化车均延误为目标，基于动态规划法协同优化网联车辆的轨迹与交叉口信号配时参数。仿真结果表明，ML-SLSG策略能有效缩短变道长度，在低、中和高这3种交通负荷下，本文提出的车辆纵向轨迹优化模型能使交叉口车均延误减少5。9%～8。0%，且与信号配时协同优化后，车均延误可再降低3。7%～22。8%。与同类方法对比研究表明，SCOM-URTF更适合多种驾驶行为相互协调的交通环境。敏感性分析显示，更高的CAV渗透率和道路限速有助于降低车均延误;增大交叉口间距可在初期减少车均延误，但达到临界点后会出现延误反弹，而轨迹与信号的协同优化能有效遏制延误的反弹。
Segmented Cooperative Control Method for Urban Road Traffic Flow in Connected Vehicle Environment
The lane-changing behavior of vehicles approaching intersections will constrain the improvement of intersection traffic efficiency.Based on this,this paper proposes a Segmented Cooperative cOntrol Method for Urban Road Traffic Flow(SCOM-URTF)in a connected vehicle environment,which adopts a bi-level optimization model to achieve dynamic division of road section functional zones and collaborative optimization of traffic flow between road section and intersection.The upper-level model designs a Misaligned Lane-changing with Separated Lane Speed Guidance(ML-SLSG)to promote rapid lane changes for left and right turning vehicles through rearranging the vehicles entering from upstream intersection in longitudinal space,minimizing the vehicle lane-changing zone length,and balancing lane group traffic flow.The lower-level model uses dynamic programming to optimize the trajectory of connected vehicles and intersection signal timing parameters with the goal of minimizing average vehicle delay.The simulation results show that ML-SLSG can effectively shorten the total length of lane-changing.At the same time,the longitudinal trajectory optimization model proposed in this paper can reduce average vehicle delays at intersections by 5.9%～8.0%under low,medium and high traffic demands.And after further collaborative optimization of vehicle trajectory and signal timing,the average vehicle delay can be further reduced by 3.7%～22.8%.Comparative studies with similar methods have shown that SCOM-URTF is more suitable for traffic environments where multiple driving behaviors are coordinated with each other.Sensitivity analysis shows that higher connected and automated vehicle penetration rates and road speed limits can help reduce average vehicle delays,and increasing the spacing between intersections can initially reduce average vehicle delay,but there may be a delay rebound after reaching the critical point.However,the coordinated optimization of trajectories and signals can effectively curb the rebound of delays.
intelligent transportationsegmented collaborative control for traffic flowdivision of road functional zonesconnected vehicletrajectory optimizationsignal timing
万方数据
维普
