联网自动车环境下的信号交叉口优化方法研究

为适应智能交通发展新趋势，改进了两个信控交叉口通行能力模型，使得新模型适用于联网自动车（connected autonomous vehicles,CAVs）环境。其一是最小延迟模型，通过最小化交叉路口的总延迟得出绿灯时间并根据流速得出周期长度，模型引入了每个车道或车道组的绿灯延时和具体排队服务时间的比率作为参数，该参数依赖于交叉路口处车辆到达的概率分布。其二是混合模型，包括两个模型，分别是估计排队服务时间的静态排队模型和从单位延时、流速、车道的限速、检测器长度中估计得到绿灯延伸时间的动态模型。数值算例表明，CAVs环境下新模型的交叉口延误显著减小，从而验证了两个通行能力模型的有效性，可为交管部门进行路口改造提供依据。

Optimization of signalized intersection in an environment of connectedautonomous vehicles

To adapt to the new development trend of intelligent transportation, two traffic capacity models of signalized intersections are improved to make them suitable for an environment of connected autonomous vehicles (CAVs). The first is the minimum delay model, which obtains the green light time by minimizing the total delay at the intersection and obtains the cycle length according to the flow rate. This model introduces the ratio of the green light delay of each lane or lane group to the specific queuing service time as a parameter, which depends on the probability distribution of vehicle arrival at the intersection. The second is the hybrid model, which combines the static queuing model that estimates the queuing service time and a dynamic model that estimates the green light extension time from the unit delay, flow rate, lane speed limit, and detector length. A numerical example shows that the delay of the new model is significantly reduced in the environment of CAVs, which validates the two capacity models and provides the basis for the traffic management department to conduct intersection reconstruction.