Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications.
翻译:最近,已努力将信号阶段和时间(SPaT)信息标准化,这些信息包含所有信号化交叉方法的信号阶段时间,因此,这些信息可用于高效的运动规划,导致交通流量更加均匀和速度剖面统一。尽管努力为半激活信号控制系统提供可靠的预测,但充分激活控制信号阶段的预测仍然具有挑战性。本文件提议使用综合通信信号和循环探测器数据建立时间序列预测框架。我们利用最先进的机器学习模型预测未来信号阶段的长度。线状回归、随机森林和长-短期-中期神经网络的性能根据天真的基线模型进行评估。根据瑞士苏黎世完全激活信号控制系统的经验数据计算的结果显示,机器学习模型超过常规预测方法。此外,基于树木的决策模型,例如RF,在符合实际应用要求的准确性方面表现最佳。