A Review of Research on Intersection Control Based on Connected Vehicles and Data-Driven Intelligent Approaches

Abstract

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.