Adaptive traffic signal control algorithms based on probe vehicle data. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Lian, Fushi; Chen, Bokui; Zhang, Kai; Miao, Lixin; Wu, Jinchao; Luan, Shichao
• Is Part Of: Journal of intelligent transportation systems. Volume 25:Issue 1(2021)
• Page Start: 41
• Page End: 57
• Publication Date: 2021-01-02
• Subjects: Adaptive signal control algorithms -- advanced traffic management systems -- intelligent transportation systems -- probe vehicle data -- proportion of nonstop group; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2020.1750384
• Languages: English
• ISSNs: 1547-2450

Abstract

Abstract: In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this indicator can be accurately estimated by data from probe vehicles. Our goal is to ease traffic congestion and enhance the capacities of traffic networks. Compared with the Webster fixed-time signal control algorithm, these two new adaptive signal control algorithms are evaluated on a microscopic simulation platform. Simulation results show that the average travel time is reduced by 32% under the iterative signal control algorithm and by 23% under the optimized signal control algorithm, and the average delay times are reduced by 36% and 35%, respectively. In the meantime, the average number of stops under the iterative signal control algorithm is reduced by 43%, and under the optimized signal control algorithm, by 67%. They indicate that the two new adaptive signal control algorithms are effective for easing traffic congestion and achieve the adaptive signal control objectives using real-time traffic information.