Traffic signals play an important role in transportation by enabling traffic flow management, and ensuring safety at intersections. In addition, knowing the traffic signal phase and timing data can allow optimal vehicle routing for time and energy efficiency, eco-driving, and the accurate simulation of signalized road networks. In this paper, we present a machine learning (ML) method for estimating traffic signal timing information from vehicle probe data. To the authors best knowledge, very few works have presented ML techniques for determining traffic signal timing parameters from vehicle probe data. In this work, we develop an Extreme Gradient Boosting (XGBoost) model to estimate signal cycle lengths and a neural network model to determine the corresponding red times per phase from probe data. The green times are then be derived from the cycle length and red times. Our results show an error of less than 0.56 sec for cycle length, and red times predictions within 7.2 sec error on average.

本文介绍了一种使用机器学习方法从车辆探测数据中估计交通信号灯定时信息的方法。研究开发了一种极端梯度提升模型来估计信号灯周期长度，并使用神经网络模型来确定对应的红灯时间。通过计算周期长度和红灯时间，可以导出绿灯时间。实验结果表明，周期长度的预测误差小于0.56秒，红灯时间的预测误差平均为7.2秒。

基于探测车辆数据的交通信号定时预测的机器学习方法