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Open AccessReview

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

by Kai Gao 1,2, Shuo Huang 1, Jin Xie 1, Neal N. Xiong 3,* and Ronghua Du 1,2
1
College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China
2
Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, China
3
Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 200093, USA
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(6), 885; https://doi.org/10.3390/electronics9060885
Received: 14 April 2020 / Revised: 11 May 2020 / Accepted: 21 May 2020 / Published: 26 May 2020
(This article belongs to the Special Issue Data Analysis in Intelligent Communication Systems)
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. View Full-Text
Keywords: intelligent transportation; CV; V2X; intelligent computing; traffic signal control; adaptive control; data-driven; reinforcement learning intelligent transportation; CV; V2X; intelligent computing; traffic signal control; adaptive control; data-driven; reinforcement learning
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Gao, K.; Huang, S.; Xie, J.; Xiong, N.N.; Du, R. A Review of Research on Intersection Control Based on Connected Vehicles and Data-Driven Intelligent Approaches. Electronics 2020, 9, 885.

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