A Hybrid Cellular Swarm Optimization Method for Traffic‐Light Scheduling. Issue 3 (1st May 2018)

• Record Type: Journal Article
• Authors: Hu, Wenbin; Wang, Huan; Yan, Liping; Du, Bo
• Is Part Of: Chinese journal of electronics. Volume 27:Issue 3(2018)
• Page Start: 611
• Page End: 616
• Publication Date: 2018-05-01
• Subjects: Transition rules -- Phase timing scheduling -- Phase shifting scheduling; cellular automata -- genetic algorithms -- particle swarm optimisation -- road traffic -- scheduling -- search problems; hybrid cellular swarm optimization method -- traffic‐light scheduling -- serious traffic problems -- traffic accidents -- congestion -- air pollution -- HCSO -- urban traffic lights -- hexible scheduling -- phase shifting scheduling -- globally dynamic scheduling -- Cellular automaton -- Dynamic cellular particle swarm optimization algorithm -- optimal phase timing scheduling -- differential search algorithm method -- genetic algorithm method -- comprehensive learning particle swarm optimization method -- random method -- traffic light scheduling -- traffic conditions; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2018.02.002
• Languages: English
• ISSNs: 1022-4653

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 optimizationmethod, 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.