A Hybrid Cellular Swarm Optimization Method for Traffic-Light Scheduling

HU Wenbin; WANG Huan; YAN Liping; DU Bo

doi:10.1049/cje.2018.02.002

Volume 27 Issue 3

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HU Wenbin, WANG Huan, YAN Liping, et al., “A Hybrid Cellular Swarm Optimization Method for Traffic-Light Scheduling,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 611-616, 2018, doi: 10.1049/cje.2018.02.002

Citation:

HU Wenbin, WANG Huan, YAN Liping, et al., “A Hybrid Cellular Swarm Optimization Method for Traffic-Light Scheduling,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 611-616, 2018, doi: 10.1049/cje.2018.02.002

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A Hybrid Cellular Swarm Optimization Method for Traffic-Light Scheduling

doi: 10.1049/cje.2018.02.002

School of Computer, Wuhan University, Wuhan 430072, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61572369, No.70901060, No.711530238) and the Hubei Province Natural Science Foundation (No.2015CFB423, No.2014CFB193).

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Corresponding author: WANG Huan (corresponding author) is currently a Ph.D. candidate. His main research interests are intelligent traffic simulation and optimization and social network analysis. (Email:cswanghuan@whu.edu.cn)
Received Date: 2015-11-27
Rev Recd Date: 2016-02-19
Publish Date: 2018-05-10

Abstract

Abstract

With increasing traffic every day, most cities in the world are facing serious traffic problems, such as traffic accidents, congestion and air pollution. Despite the recent improvement of urban infrastructure, reasonable traffic light scheduling still plays an important role in alleviating these traffic problems. It is a great challenge to schedule a huge number of traffic lights efficiently. To solve this problem, we propose a Hybrid cellular swarm optimization method (HCSO) to optimize the scheduling of urban traffic lights. HCSO achieves an efficient and flexible scheduling, which includes the phase timing scheduling and the phase shifting scheduling. To formulate effective solutions for various traffic problems and achieve a globally dynamic scheduling, flexible and concise transition rules based on Cellular automaton (CA) are defined. And the Dynamic cellular particle swarm optimization algorithm (DCPSO) is proposed to find the optimal phase timing scheduling efficiently. Moreover, compared with the differential search algorithm method, the genetic algorithm method, the particle swarm optimization method, the comprehensive learning particle swarm optimization method and the random method in real cases, extensive experiments reveal that HCSO achieves obvious improvements under different traffic conditions.
- Transition rules,
- Phase timing scheduling,
- Phase shifting scheduling

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References(23)

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