Paper Search

Complex Systems and Complexity Science >

2021 , Vol. 18 >Issue 3: 80 - 87

DOI: https://doi.org/10.13306/j.1672-3813.2021.03.012

Regional Traffic Signal Optimal Control Based on Improved NSGA-Ⅱ

  • MOU Liang ,
  • ZHAO Hong ,
  • LI Yan ,
  • QIU Junzheng ,
  • CUI Xiangyu ,
  • YUAN Huantao ,
  • MOU Liang ,
  • ZHAO Hong ,
  • LI Yan ,
  • QIU Junzheng ,
  • CUI Xiangyu ,
  • YUAN Huantao
Expand
  • College of Mechanical and Electrical Engineering, Qingdao University,Qingdao 266071, China

Received date: 2020-11-19

  Revised date: 2020-12-04

  Online published: 2021-06-18

Fold

Abstract

With the increasing number of motor vehicles, people pay more and more attention to the efficiency of travel and environmental problems. To solve this problem, an improved fast non dominated sorting genetic algorithm (NSGA-Ⅱ) is proposed and applied to the optimization of regional traffic signal timing. The algorithm is a multi-objective optimization algorithm, which takes the average delay and exhaust emissions of regional traffic as the optimization objectives. After the algorithm optimization, a series of optimal values will be obtained, from which the comprehensive optimal value is selected. Finally, VISSIM microscopic traffic simulation software is used to build a simulation model for this case, and the optimization results of the method are verified. The effectiveness of the proposed method is verified by comparing it with the improved fast non dominated sorting genetic algorithm. The simulation results show that the optimized timing reduces the CO emission by 8.213% and the average vehicle delay by 19.023%.

Key words： regional traffic; improvement of NSGA-Ⅱ; multi-objective optimization; vehicle delay; exhaust emission

Cite this article

MOU Liang , ZHAO Hong , LI Yan , QIU Junzheng , CUI Xiangyu , YUAN Huantao , MOU Liang , ZHAO Hong , LI Yan , QIU Junzheng , CUI Xiangyu , YUAN Huantao . Regional Traffic Signal Optimal Control Based on Improved NSGA-Ⅱ[J]. Complex Systems and Complexity Science, 2021 , 18(3) : 80 -87 . DOI: 10.13306/j.1672-3813.2021.03.012

References

[1] 雷洋, 黄承锋. 城市交通拥堵治理的研究综述和建议[J]. 综合运输,2018, 40(4): 8-11,42.
Lei Yang, Huang Chengfeng. Research summary and suggestions on urban traffic congestion control[J]. Comprehensive Transportation, 2018, 40(4): 8-11,42.
[2] 赵彪.面向全路网交通流优化的交通灯调度方法研究[D]. 上海:华东师范大学,2016.
Zhao Biao.Research on traffic light scheduling method for traffic flow optimization of the whole road network[D]. Shanghai: East China Normal University, 2016.
[3] Vilarinho C,Tavares J P,Rossetti R J F.Design of a multiagent system for real-time traffic control[J].IEEE Intelligent Systems,2016,31(4):68-80, 1541-1672.
[4] Bie Y,Liu Z,Wang Y.A real-time traffic control method for the intersection with pre-signals under the phase swap sorting strategy[J].PLoS ONE,2017,12(5): 1932-6203.
[5] Liakou H M. An overview on transports and ITS[J]. Communications in Computer and Information on Science, 2010, 112: 343-345, 1865-0929.
[6] 李松, 刘力军, 解永乐. 遗传算法优化 BP 神经网络的短时交通流混沌预测[J]. 控制与决策,2011, 26(10):1581-1585.
Li Song, Liu Lijun, Xie Yongle. Chaotic prediction for short-termtraffic flow of optimized BP neural network based ongenetic algorithm[J]. Control and Decision, 2011, 26(10):1581-1585.
[7] 闫东宇, 邢双云, 操峻岩, 等. 交通信号灯优化控制[J].科技与创新,2018(3): 16-18.
Yan Dongyu, Xing Shuangyun, Cao Junyan, et al. Optimized control of traffic lights[J]. Science and Technology & Innovation,2018(3): 16-18.
[8] 龙小强, 李捷, 陈彦如. 基于深度学习的城市轨道交通短时客流量预测[J]. 控制与决策,2019, 34(8):1589-1600.
Long Xiaoqiang, Li Jie, Chen Yanru. Metro short-term traffic flow prediction with deep Learning[J]. Control and Decision,2019, 34(8): 1589-1600.
[9] 张文泉. 城市区域交通信号智能控制算法分析与研究[D]. 四川: 西南交通大学信息科学与技术学院, 2016:16-26.
Zhang Wenquan. Analysis and research on intelligent control algorithm of urban area traffic signal[D].Sichuan: School of Information Science and Technology,Southwest Jiaotong University, 2016: 16-26.
[10] 柳长源,任宇艳,毕晓君.基于改进萤火虫算法的区域交通信号配时优化[J].控制与决策, 2020, 35(12): 2829-2834.
Liu Changyuan, Ren Yuyan, Bi Xiaojun. Regional traffic signal timing optimization based on improved firefly algorithm[J]. Control and Decision, 2020, 35(12): 2829-2834.
[11] 陈海洋,金晓磊,牛龙辉,等.基于改进克隆选择算法的区域交通灯配时优化[J].计算机工程与应用,2020,56(9):272-278.
Chen Haiyang,Jin Xiaolei, Niu Longhui, et al. Regional traffic light timing optimization based on improved clonal selection algorithm[J]. Computer Engineering and Application, 2020,56(9): 272-278.
[12] 耿焕同,李辉健,赵亚光,等.基于自适应混合非支配个体排序策略的改进型NSGA-Ⅱ算法[J].计算机应用,2016,36(5):1319-1324,1340.
Geng Huantong, Li Huijian, Zhao Yaguang, et al. Improved NSGA-Ⅱ algorithm based on adaptive hybrid non dominated individual sorting strategy[J]. Computer Application, 2016,36(5): 1319-1324,1340.
[13] 彭敏,单铮,于泉,等.基于博弈的交通信号配时优化[J].公路,2019,64(8):268-273.
Peng min, Dan Zheng, Yu Quan, et al. Game based traffic signal timing optimization[J]. Highway, 2019,64(8): 268-273.
[14] 卫星,张利,魏振春,等.交通信号自适应遗传控制算法及其仿真研究[J].系统仿真学报,2012,24(11):2255-2258.
Wei Xing, Zhang Li, Wei Zhenchun, et al. Research on adaptive genetic control algorithm and Simulation of traffic signal[J]. Journal of System Simulation, 2012,24(11): 2255-2258.
[15] 唐少虎,周进,尚春琳,等.降雨情景下城市道路交通信号控制优化模型与方法[J].系统仿真学报,2020,32(2):149-156.
Tang Shaohu, Zhou Jin, Shang Chunlin, et al. Optimization model and method of urban road traffic signal control under rainfall scenario[J]. Journal of System Simulation, 2020,32(2): 149-156.
[16] 刘权富,陆百川,马庆禄,等.平面交叉口信号控制多目标优化研究[J].交通科技与经济,2014,16(1):47-50.
Liu Quanfu, Lu Baichuan, Ma Qinglu, et al. Study on multi-objective optimization of signal control at intersections[J]. Transportation Technology and Economy, 2014,16(1): 47-50.
[17] 陈亮,池维源,李巧茹,等.基于交通运输高质量发展的区域交通碳排放影响因素研究[J].公路,2019,64(6):171-176.
Chen Liang, Chi Weiyuan, Li qiaoru, et al. Study on the influencing factors of regional traffic carbon emission based on the high-quality development of transportation[J]. Highway, 2019,64(6): 171-176.
[18] 周申培. 考虑排放因素的城市交叉口交通信号控制策略的研究[D]. 武汉: 武汉理工大学,2009.
Zhou Shenpei. Study on traffic signal control strategy of urban intersections considering emission factors[D]. Wuhan: Wuhan University of Technology, 2009.
[19] 汪镭,张茂清,郭为安,等.基于强化交叉算子的快速非支配排序遗传算法Ⅱ[J].南昌工程学院学报,2020,39(1):10-14.
Wang Lei, Zhang Maoqing, Guo Wei'an, et al. Fast non dominated sorting genetic algorithm based on enhanced crossover operator Ⅱ[J]. Journal of Nanchang Institute of Engineering, 2020,39(1): 10-14.
[20] 蒋张敏,罗文坚,王煦法.一种基于正态分布交叉的ε - MOEA[J].软件学报,2009, 20(2): 305-314.
Jiang Zhangmin, Luo Wenjian, Wang Xufa. An ε-MOEA based on normal distribution crossover[J]. Acta Software Sinica, 2009, 20(2): 305-314.
[21] Chen M Y. Modeling and simulation analysis of road network based on VISSIM[C]. Proceedings of 2019 International Coference on Intelligent Transportation, Big Data & Smart City. Changsha, China, 2019.
Options
Outlines

/

〈 〉

Copyright © 2017 Editorial Office of Complex Systems and Complexity Science
Tel.：0532-85953597 E-mail:fzkxbjb@qdu.edu.cn