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Title: Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm
Authors: Jia, Hongfei
Lin, Yu
Luo, Qingyu
Li, Yongxing
Miao, Hongzhi
Keywords: Signal Timing
Per Capita Delay
DRNTU::Engineering::Civil engineering
Issue Date: 2019
Source: Jia, H., Lin, Y., Luo, Q., Li, Y., & Miao, H. (2019). Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm. Advances in Mechanical Engineering, 11(4), 1-9. doi:10.1177/1687814019842498
Series/Report no.: Advances in Mechanical Engineering
Abstract: Currently, signal control mode is the main control method of urban road intersections. Given that the traffic efficiency of road intersections is mainly affected by signal timing schemes, it is important to optimize signal timing at road intersections. Therefore, signal timing optimization methods of urban road intersections are explored in this work. When optimizing the timing of the signal at the intersections, the selection of optimization targets play an important role. At present, there are multiple objectives considered while designing signal timing scheme, including capacity, delays, and automobile exhaust. However, from the perspective of the traveler, they are more concerned about their own delay while passing intersections. In this work, we propose a novel multi-objective signal timing optimization model with goals of per capita delay, vehicle emissions, and intersection capacity. Considering the problem characteristics of the target problem, a meta-heuristic algorithm combining difference operator, which is based on Particle Swarm Optimization Algorithm, is developed. To test the validity of proposed approach, we applied it to real-world intersection signal timing problems in China. The results show that the optimized signal timing scheme obtained by the proposed algorithm is better than the realistic one. Also, the effectiveness of the developed algorithm is demonstrated by comparing it with other efficient algorithms.
ISSN: 1687-8132
DOI: 10.1177/1687814019842498
Rights: © 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (
Fulltext Permission: open
Fulltext Availability: With Fulltext
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