Benefiting from the application of vehicle communication networks and new technologies, such as connected vehicles, video monitoring, automated vehicles and vehicle–road collaboration, traffic network data can be observed in real-time. Applied in the field of traffic control, these technologies can provide high-quality input data and make a more comprehensive evaluation of the effectiveness of traffic control. However, most of the control theories and strategies adopted by adaptive control systems cannot effectively use these real-time, high-precision data. In order to adapt to the development of the times, intersection control theory needs to be further developed. This paper reviews the intersection control strategies from many perspectives, including intelligent data-driven control, conventional timing control, induction control and model-based traffic control. There are three main directions for intersection control based on the connected vehicle environment: (1) data-driven reinforcement learning control; (2) adaptive performance optimization control; (3) research on traffic control based on the environment of connected vehicles (CV); and (4) multiple intersection control based on the CV environment. The review gives a clear view of the data-driven intelligent control theory and its application for intelligent transportation systems.

中文翻译：

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基于互联车辆和数据驱动智能方法的交叉口控制研究综述

得益于车辆通讯网络和新技术的应用，例如互联车辆，视频监控，自动车辆和车路协作，交通网络数据可以实时观察。这些技术应用于交通控制领域，可以提供高质量的输入数据，并对交通控制的有效性进行更全面的评估。但是，自适应控制系统采用的大多数控制理论和策略都不能有效地使用这些实时，高精度数据。为了适应时代的发展，交叉路口控制理论需要进一步发展。本文从多个角度回顾了交叉路口控制策略，包括智能数据驱动控制，常规时序控制，归纳控制和基于模型的交通控制。基于互联车辆环境的交叉路口控制主要有三个方向：（1）数据驱动的强化学习控制；（2）自适应性能优化控制；（3）基于互联车辆环境的交通控制研究；（4）基于CV环境的多重路口控制。这篇综述清楚地了解了数据驱动的智能控制理论及其在智能交通系统中的应用。（4）基于CV环境的多重路口控制。这篇综述清楚地了解了数据驱动的智能控制理论及其在智能交通系统中的应用。（4）基于CV环境的多重路口控制。这篇综述清楚地了解了数据驱动的智能控制理论及其在智能交通系统中的应用。