基于车路协同环境的信号交叉口车速引导方法

针对现有的车速引导模型存在未综合考虑车辆跟驰行为、引导场景划分较粗略等问题，研究了4种基于车路协同环境下实时优化各车的车速引导模型。对车辆进行所属车辆列队划分，考虑车速引导影响对FVD跟驰模型进行改进。以车辆列队为引导单元，将车辆可能面临的交通状况细分为8种引导场景，以引导车辆不停车或少停车通过交叉口为目标，直接优化车辆加/减速度，建立车辆列队后车根据改进的跟驰模型计算目标跟驰加/减速度，并与头车组成列队以同一目标车速通过交叉口停车线的4种车速引导模型。以南昌市海棠北路/枫林西大街交叉口为例进行仿真验证，结果表明，所提出的车速引导模型能使车辆行程时间减少18.9%，最大排队长度减少58.8%，延误减少60.8%，燃油消耗减少36.4%，且适用于不同交通饱和状态，对提高信号交叉口通行效率和减少车辆燃油消耗有显著效果。 

A Speed Guidance Method at Signalized Intersections Based on Vehicle Infrastructure Cooperation

The existing speed guidance models for vehicles are not comprehensively considering the car-following behaviors and the roughdivision of guidance scenes. This paper studies four speed guidance models based on real-time optimization of eachvehicle in vehicle infrastructure cooperation. The vehicles are divided into queues, and the FVD car-following model is improved considering the influences of speed guidance. The vehicle platoon is used as the guidance unit, and the traffic conditions are subdivided into eight guidance scenarios. Withthe goals of optimizing the operating efficiency of the intersection without stopping or withless stopping, four vehicle speed guidance models are established to optimize vehicle acceleration/deceleration by calculating the target acceleration/deceleration based on the improved car-following model, that the following vehicles can cross the intersection stop line in a platoon at the same target speed withthe leading vehicle. The intersection between Haitang NorthRoad and Fenglin West Street in Nanchang is taken as a case study for verification. The results show that the proposed model can reduce vehicle travel time by 18.9%, maximum queue lengthby 58.8%, delay by 60.8%, and fuel consumption by 36.4%. It is suitable for different traffic saturation conditions, significantly improving the traffic at signalized intersections and reducing vehicle fuel consumption.