面向常发拥堵点的主动交通诱导方法

Active Traffic Guidance Method for Recurrent Congestion Points

基于动态用户均衡、系统最优分配的诱导方法,侧重路网需求的宏观预测和调节,难以准确辨识道路拥堵点的关联车流,制约了诱导效果。为精准调控致堵车流,有效缓解常发性拥堵,研究基于需求溯源的主动交通诱导方法。遵循靶向诱导的思路,分析车辆行驶轨迹和常发拥堵点的交通流关联性,运用卡尔曼滤波对关联车流进行短时预测,在此基础上,结合流量占比、路径饱和度等指标,对诱导目标车流进行优选。同时,从负荷均衡的角度出发,基于路段与路径交通流的时空关联更新路网交通状态,建立以饱和度均衡为目标的主动诱导优化模型。仿真结果表明:相比反应型诱导与基于路径偏好的主动型诱导,所提方法使常发拥堵点的车均延误、停车次数等下降30%~60%,路网车均延误、停车次数等下降10%~15%,模型收敛速度提高,交通效益提升,验证了该方法的有效性。

Traffic guidance methods based on dynamic user equilibrium and optimal system allocation focus on macro forecasting and adjustment of road network demands.They are difficult to accurately identify related traffic flow,which restricts the guidance effects.An active traffic guidance method based on traceable demand is proposed to con⁃trol traffic flows and alleviate recurrent congestion.The study following the idea of targeted guidance,analyzes the cor⁃relation between vehicle trajectory and traffic flow at frequent congestion points,and uses Kalman filter to make short-term predictions of the associated traffic flow.Furthermore,it is preferable to optimize target traffic flows in com⁃bination with indicators such as traffic ratio and path saturation.Meanwhile,based on balanced traffic distribution,the spatio-temporal correlation between the road section and the path traffic flow is used to update the road network traffic state and establish an active induction optimization model with saturation equilibrium as the goal.The simula⁃tion results show that,compared with the active induction based on path preference,this method result in a reduction from 30%to 60%in the average delay and the number of stops of vehicles at frequent congestion points,and a reduc⁃tion from 10%to 15%in the whole road network.The convergence speed and traffic benefit of the model are signifi⁃cantly improved,which verifies the effectiveness of the method in the work.