车路协同环境下信号交叉口车速引导建模与仿真

目前交通自适应控制策略中预测交通流到达的方法多为基于车流行驶速度服从统一分布而获得,其效率与可靠性等方面存在不足.文中利用车路协同环境下实时车车、车路通信,基于实时信号状态、排队长度、车辆位置、加速度等参数,以交叉口车辆停车时间最小化为目标,提出面向个体车辆的车速引导机制与模型,有效弥补了上述缺陷.以上海市曹安路嘉松北路交叉口为例进行仿真验证,结果表明,在高饱和状态下,文中模型能有效降低交叉口车均延误30％,减少交叉口平均停车次数60％,在中、低饱和状态下的效益更佳.

Modeling Vehicle Speed Guidance at Signalized Intersections under IntelliDriverSM

Most methods to forecast traffic flow arrivals in the adaptive control strategy assume that traffic flow speed is subject to uniform distribution.However,these methods have some disadvantages over efficiency and reliability.To deal with this,this paper uses real-time vehicle-to-vehicle and vehicle-to-road communications provided by IntellidriverSM to obtain minimum vehicle delay at intersections.Then,it proposes a vehicle speed guidance mechanism to model individual vehicles based on real-time signal state,queue length,vehicle location and acceleration.Using a simulation verification example at the intersection of Caoan Road and North Jiasong Road in Shanghai,this paper concludes that under heavy traffic flows,the model can efficiently reduce the average vehicle delay at intersections by 30%;it can decrease the average number of vehicle stops at intersections by 60%.Better results can be obtained for medium or low level of saturation.