Abstract In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this indicator can be accurately estimated by data from probe vehicles. Our goal is to ease traffic congestion and enhance the capacities of traffic networks. Compared with the Webster fixed-time signal control algorithm, these two new adaptive signal control algorithms are evaluated on a microscopic simulation platform. Simulation results show that the average travel time is reduced by 32% under the iterative signal control algorithm and by 23% under the optimized signal control algorithm, and the average delay times are reduced by 36% and 35%, respectively. In the meantime, the average number of stops under the iterative signal control algorithm is reduced by 43%, and under the optimized signal control algorithm, by 67%. They indicate that the two new adaptive signal control algorithms are effective for easing traffic congestion and achieve the adaptive signal control objectives using real-time traffic information.

中文翻译：

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基于探测车辆数据的自适应交通信号控制算法

摘要 本文基于探测车辆的数据提出了两种新的自适应交通信号控制算法，以实现干线道路的协调信号控制。一种是迭代信号控制算法，另一种是优化信号控制算法。选择直达组中车辆的比例作为交通状况的指标。该指标的值可以通过探测车辆的数据准确估计。我们的目标是缓解交通拥堵，提高交通网络的容量。与韦氏固定时间信号控制算法相比，这两种新的自适应信号控制算法在微观仿真平台上进行了评估。仿真结果表明，迭代信号控制算法平均走时减少32%，优化信号控制算法减少23%，平均延迟时间分别减少36%和35%。同时，迭代信号控制算法下的平均停靠次数减少了43%，优化后的信号控制算法下，平均停靠次数减少了67%。他们表明这两种新的自适应信号控制算法可以有效缓解交通拥堵，并利用实时交通信息实现自适应信号控制目标。在优化的信号控制算法下，提高了 67%。他们表明这两种新的自适应信号控制算法可以有效缓解交通拥堵，并利用实时交通信息实现自适应信号控制目标。在优化的信号控制算法下，提高了 67%。他们表明这两种新的自适应信号控制算法可以有效缓解交通拥堵，并利用实时交通信息实现自适应信号控制目标。