Speed advisory systems for connected vehicles rely on the estimation of green (or red) light duration at signalized intersections. A particular challenge is to predict the signal phases of semi- and fully-actuated traffic lights. In this paper, we introduce an algorithm that processes traffic measurement data collected from advanced detectors on road links and assigns "PASS"/"WAIT" labels to connected vehicles according to their predicted ability to go through the upcoming signalized intersection within the current phase. Additional computations provide an estimate for the duration of the current green phase that can be used by the Speed Advisory System to minimize fuel consumption. Simulation results show 95% prediction accuracy, which yields up to 30% reduction in fuel consumption when used in a driver-assistance system. Traffic progression quality also benefits from our algorithm demonstrating an improvement of 20% at peak for medium traffic demand, reducing delays and idling at intersections.

中文翻译：

---

使用实时驱动交通灯相位长度预测的速度咨询系统

联网车辆的速度咨询系统依赖于信号交叉口绿（或红）灯持续时间的估计。一个特别的挑战是预测半驱动和全驱动交通灯的信号相位。在本文中，我们介绍了一种算法，该算法处理从道路连接上的高级检测器收集的交通测量数据，并根据连接车辆在当前阶段通过即将到来的信号灯交叉口的预测能力为它们分配“通过”/“等待”标签。额外的计算提供了当前绿色阶段的持续时间的估计值，速度咨询系统可以使用它来最小化燃料消耗。仿真结果显示 95% 的预测精度，当用于驾驶员辅助系统时，可将油耗降低 30%。