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.

翻译：交通信号在交通管理中扮演重要角色，不仅实现车流管理，还保障交叉口安全。此外，掌握交通信号相位与配时数据，可优化车辆路径规划以实现时间与能效、生态驾驶，并支持信号化路网的精确仿真。本文提出一种基于车辆探针数据估算交通信号配时的机器学习方法。据作者所知，目前极少有研究采用机器学习技术从车辆探针数据中确定交通信号配时参数。本研究构建了极端梯度提升（XGBoost）模型以估计信号周期长度，并开发神经网络模型从探针数据中确定各相位对应的红灯时间，进而根据周期长度与红灯时间推导绿灯时间。结果表明，周期长度预测误差小于0.56秒，红灯时间预测平均误差在7.2秒以内。