智能网联测试场混合交通流仿真研究

Simulation Research of Mixed Traffic Flow in Intelligent Network Test Field

智能网联汽车的出现为解决交通问题提供了新的技术手段。通过智能网联汽车与交通控制协同策略消除交通冲击波的生成和传播影响是提高交通效率与安全的有效手段之一。研究对混合流下的智能网联车辆速度控制与可变限速控制以及两者的协同控制模型与机制,通过搭建测试场数字孪生感知平台,获取测试场高环区域内的实时混合交通流数据,并构建协同控制模型框架与最优化求解办法,探究协同管控策略在测试场高环场景下对交通效率与安全的实际效果。通过仿真实验,对比交通效率、安全等核心指标,显示的协同控制策略对测试场交通流碰撞时间分布数据优化了50%以上,对当前场景的总行程时间优化超过20%,为智能网联车辆控制优化交通流提供解决思路。

The emergence of intelligent connected vehicles has provided new technological means to solve traffic problems.Eliminating the generation and propagation of traffic shock waves through the collaborative strategy of intelligent connected vehicles and traffic control is one of the effective means to improve traffic efficiency and safety.The speed control and variable speed limit control of intelligent connected vehicles under mixed flow are studied,as well as the collaborative control model and mechanism of the two.By building a digital twin perception platform for the test field,real-time mixed traffic flow data in the high loop area of the test field is obtained,and a collaborative control model framework and optimization solution are constructed to explore the actual effects of collaborative control strategies on traffic efficiency and safety in the high loop scenario of the test field.Through simulation experiments,core indicators such as traffic efficiency and safety were compared,and it was found that the collaborative control strategy optimized the collision time distribution data of the test field traffic flow by more than 50%,and optimized the total travel time of the current scenario by more than 20%.This provides a solution for optimizing traffic flow through intelligent connected vehicle control.