Vehicle-to-Infrastructure-Based Traffic Signal Optimization for Isolated Intersection

Yingjun Qiao, Tianchuang Meng (), Hongmao Qin, Ziniu Hu and Zhihua Zhong
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Yingjun Qiao: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 200092, China
Tianchuang Meng: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Hongmao Qin: Wuxi Institute of Intelligent Control, Hunan University, Wuxi 214115, China
Ziniu Hu: School of Machinery and Transportation Engineering, Hunan University, Changsha 410082, China
Zhihua Zhong: Center for Strategic Studies, Chinese Academy of Engineering, Beijing 100088, China

Sustainability, 2023, vol. 15, issue 8, 1-13

Abstract: Traffic signal control is critical for traffic efficiency optimization but is usually constrained by traffic detection methods. The emerging V2I (Vehicle to Infrastructure) technology is capable of providing rich information for traffic detection, thus becoming promising for traffic signal control. Based on parallel simulation, this paper presents a new traffic signal optimization method in a V2I environment. In the proposed method, a predictive optimization problem is formulated, and a cellular automata model is employed as traffic flow model. By using genetic algorithm, the predictive optimization problem is solved online to implement receding horizon control. Simulation results show that the proposed method can improve traffic efficiency in the sense of reducing average delay and number of stops. Meanwhile, simulation also shows that greater communication range brings better performance for reducing the average number of stops. Simulation results show that the proposed V2I-based signal control method can improve traffic efficiency, especially when the traffic volume is relatively high. The proposed algorithm can be applied to traffic signal control to improve traffic efficiency.

Keywords: V2I; traffic signal optimization; genetic algorithm; horizon control; traffic detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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