信号交叉口人机混驾交通流速度控制策略建模

为分析人机混驾交通流下网联自动驾驶车辆（connected and autonomous vehicles,CAV）速度控制策略对交通流运行特征的影响，构建了考虑驾驶员对行车信息获取不确定性的人工驾驶车辆交叉口通行决策模型。提出考虑前车速度影响的自动驾驶速度控制策略，构建信号交叉口连续型元胞自动机更新规则，通过引入不同CAV渗透率、道路饱和度、控制区长度参数，研究CAV速度控制策略对信号交叉口交通流运行特征的影响。结果表明：CAV能显著提高交叉口通行能力，且车流通过交叉口区域的延误显著降低；同时速度控制策略的实施效果还受控制区长度的影响，呈现出随着控制区长度的增加，车均延误逐渐降低并趋于稳定。

Modeling of Traffic Flow Velocity Control Strategy for Human-machine Mixed Driving at Signalized Intersections

In order to analyze the influence of speed control strategy of autonomous vehicle on the operation characteristics of traffic flow, a deterministic decision-making model for intersections with artificially driven vehicles considering the driver's influence on the acquisition of driving information is constructed. An automatic driving speed control strategy considering the influence of the speed of preceding vehicle is proposed, and the continuous Cellular Automata update rules for signalized intersections are constructed respectively. By introducing the different penetration rates of automatic driving, road saturation and control area length parameters, the influence of CAV speed control strategy on the traffic flow characteristics of signalized intersections is studied. The results show that the autonomous vehicle can significantly improve the traffic capacity of intersection, and the delay of traffic flow through the intersection area is significantly reduced. The implementation effect of the speed control strategy is also affected by the length of control area, which shows that following the increase of length of control area, the average vehicle delay gradually decreases and stabilizes.