效率公平防溢并重的多交叉口协调控制

doi:10.16381/j.cnki.issn1003-207x.2022.2051

中国管理科学 ›› 2023, Vol. 31 ›› Issue (7): 214-226.doi: 10.16381/j.cnki.issn1003-207x.2022.2051

效率公平防溢并重的多交叉口协调控制

乔健¹, 陈梦甜¹, 何梦莹¹, 陈炘钧²

1.西北工业大学管理学院，陕西西安710072；2.亚利桑那大学埃勒管理学院，亚利桑那图森85721

收稿日期:2022-09-16 修回日期:2022-10-31 出版日期:2023-07-17 发布日期:2023-07-17
通讯作者: 乔健(1965-)，男(汉族)，江苏宝应人，西北工业大学管理学院，副教授，博士生导师，博士，研究方向：智能交通管理，Email：qiaojian@nwpu.edu.cn. E-mail:qiaojian@nwpu.edu.cn
基金资助:
国家自然科学基金资助面上项目(71971171)

A Multi-intersection Coordinated Control with Equal Emphasis on Efficiency, Fairness, and Spillback

QIAO Jian¹, CHEN Meng-tian¹, HE Meng-ying¹, CHEN Xin-jun²

1. School of Management, Northwestern Polytechnical University, Xi’an 710072, China; 2. Eller College of Management, University of Arizona, Tucson, AZ 85721, USA

Received:2022-09-16 Revised:2022-10-31 Online:2023-07-17 Published:2023-07-17
Contact: 乔健 E-mail:qiaojian@nwpu.edu.cn

摘要/Abstract

摘要： 在网络交通控制中，效率公平防溢并重仍然是个开放问题。本文采用模糊逻辑、遗传算法和多Agent系统结合的规则驱动方法构建了效率公平防溢并重的多交叉口协调控制模型，其中每个交叉口的交通信号由一个Agent控制，每个Agent包含四个功能模块：信息集散模块负责收发本地和相邻交叉口的交通状况信息，相位选择模块负责选择下一个通行相位，时长计算模块负责为下一个通行相位计算绿灯时间，决策优化模块负责优化相位选择模块的通行紧急度模糊推理机和时长计算模块的绿灯时间模糊推理机的模糊规则和隶属函数。实验结果表明，优化上述模糊推理机能显著提升模型性能，效率公平并重的独立控制比定时控制和感应控制更高效更公平，效率公平防溢并重的协调控制不仅比独立控制更高效更公平，而且能有效防止发生车辆回溢，同时还能使网络交通流量向分布更加均衡的方向发展。

关键词: 网络交通；协调控制；效率；公平；防溢

Abstract: To pay equal attention to efficiency, fairness, and spillback in the process of network traffic control remains an open problem. A multi-intersection coordinated control model with equal emphasis on efficiency, fairness and spillback is constructed by using a rule-driven method that combines fuzzy logic, genetic algorithm, and multi-agent system, where the traffic signal of each intersection is controlled by an agent that contains four modules, the information collection and distribution module is responsible for collecting and sending traffic condition at local and adjacent intersections, the phase selection module is responsible for selecting the next green phase, the green time calculation module is responsible for calculating the green time for the selected phase, and the decision optimization module is used to optimize the fuzzy rules and membership functions of both the traffic urgency fuzzy inference machine of the phase selection module and the green time fuzzy inference machine of the green time calculation module. Experimental results show that optimizing the aforementioned fuzzy inference machines can significantly improves the performance of the model, independent control with equal emphasis on efficiency and fairness is more efficient and fairer than fixed-time control and vehicle-actuated control, and coordinated control with equal emphasis on efficiency, fairness and spillback is not only more efficient and fairer than independent control, but also effectively prevents spillback, and makes the distribution of network traffic flow develop in a more balanced direction.

Key words: network traffic; coordinated control; efficiency; fairness; spillback

中图分类号:

U491.1

乔健, 陈梦甜, 何梦莹, 陈炘钧. 效率公平防溢并重的多交叉口协调控制[J]. 中国管理科学, 2023, 31(7): 214-226.

QIAO Jian, CHEN Meng-tian, HE Meng-ying, CHEN Xin-jun. A Multi-intersection Coordinated Control with Equal Emphasis on Efficiency, Fairness, and Spillback[J]. Chinese Journal of Management Science, 2023, 31(7): 214-226.

参考文献

［1］ Tirachini A, Hensher D A, Rose J M. Multimodal pricing and optimal design of urban public transport: the interplay between traffic congestion and bus crowding［J］. Transportation Research Part B: Methodological, 2014, 61: 33-54.
［2］ Koonce P, Rodegerdts L. Traffic signal timing manual［R］. United States: Federal Highway Administration, 2008.
［3］ Cools S-B, Gershenson C, D’Hooghe B. Self-organizing traffic lights: a realistic simulation［M］//Prokopenko M. Advances in applied self-organizing systems. Berlin-Heidelberg: Springer, 2013: 45-55.
［4］ Luk J. Two traffic responsive area traffic control methods: SCAT and SCOOT［J］. Traffic Engineering & Control, 1984, 25(1): 14.
［5］ Pappis C P, Mamdani E H. A fuzzy logic controller for a trafc junction［J］. IEEE Transactions on Systems, Man, and Cybernetics, 1977, 7(10): 707-717.
［6］陈淑燕, 陈森发, 吴明赞. 单路口交通的多相位实时模糊控制［J］. 系统工程理论与实践, 2003, 23(1): 110-115.Chen Shuyan, Chen Senfa, Wh Mingzan. Real-time fuzzy control for multiphase traffic signals in isolated intersection［J］. Systems Engineering-Theory & Practice, 2003, 23(1): 110-115.
［7］ Nakatsuyama M, Nagahashi H, Nishizuka N. Fuzzy logic phase controller for traffic junctions in the one-way arterial road［J］. IFAC Proceedings Volumes, 1984, 17(2): 2865-2870.
［8］ Bi Yunrui, Lu Xiaobo, Sun Zhe, et al. Optimal type-2 fuzzy system for arterial traffic signal control［J］. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(9): 3009-3027.
［9］ Nakatsuji T, Kaku T. Development of a self-organizing traffic control system using neural network models［J］. Transportation Research Record, 1991, 1324: 137-145.
［10］承向军, 贺振欢, 杨肇夏. 基于遗传算法的交通信号机器学习控制方法［J］. 系统工程理论与实践, 2004, 24(8): 130-135.Cheng Xiangjun, He Zhenhuan, Yang Zhaoxia. Machine-learning traffic signal control approach based on Genetic Algorithm［J］. Systems Engineering-Theory & Practice, 2004, 24(8): 130-135.
［11］ Foy M D, Benekohal R F, Goldberg D E. Signal timing determination using genetic algorithms［J］. Transportation Research Record, 1992(1365): 108-115.
［12］ Ceylan H, Bell M G. Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing［J］. Transportation Research Part B: Methodological, 2004, 38(4): 329-342.
［13］ Thorpe T L, Anderson C W. Traffic light control using sarsa with three state representations ［R］. Princeton, NJ, USA: Citeseer, 1996.
［14］马寿峰, 李英, 刘豹. 一种基于 agent 协调的两路口交通控制方法［J］. 系统工程学报, 2003, 18(3): 272-278.Ma Shoufeng, Li Ying, Liu Bao. Agent-based traffic coordination control method for two adjacent intersections［J］. Journal of Systems Engineering, 2003, 18(3): 272-278.
［15］ Camponogara E, Kraus W. Distributed learning agents in urban traffic control ［C］//Proceedings of the Portuguese Conference on Artificial Intelligence, Beja, Portugal, December 4-7, 2003.
［16］ Bingham E. Reinforcement learning in neurofuzzy traffic signal control［J］. European Journal of Operational Research, 2001, 131(2): 232-241.
［17］ Choy M C, Srinivasan D, Cheu R L. Cooperative, hybrid agent architecture for real-time traffic signal control［J］. IEEE Transactions on Systems, Man, and Cybernetics-Part A: systems and humans, 2003, 33(5): 597-607.
［18］ Mikami S, Kakazu Y. Genetic reinforcement learning for cooperative traffic signal control［C］//Proceedings of the IEEE Conference on Evolutionary Computation, Orlando, FL, June 27-29, 1994.
［19］ Spall J C, Chin D C. Traffic-responsive signal timing for system-wide traffic control［J］. Transportation Research Part C: Emerging Technologies, 1997, 5(3-4): 153-163.
［20］ Chu Tianshu, Qu Shuhui, Wang Jie. Large-scale traffic grid signal control with regional reinforcement learning［C］//Proceedings of the 2016 American Control Conference, Boston, MA, July 6-8, 2016.
［21］ Gokulan B P, Srinivasan D. Distributed geometric fuzzy multiagent urban traffic signal control［J］. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3): 714-727.
［22］ Srinivasan D, Choy M C, Cheu R L. Neural networks for real-time traffic signal control［J］. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(3): 261-272.
［23］ Wiering M A. Multi-agent reinforcement learning for traffic light control ［C］//Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, June 29 - July 2, 2000.
［24］ Prashanth L, Bhatnagar S. Reinforcement learning with function approximation for traffic signal control［J］. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 412-421.
［25］ Tan Tian, Chu Tianshu, Peng Bo, et al. Large-scale traffic grid signal control using decentralized fuzzy reinforcement learning ［C］//Proceedings of the SAI Intelligent Systems Conference, London, September 21-22, 2016.
［26］ Wang Xiaoqiang, Ke Liangjun, Qiao Zhimin, et al. Large-scale traffic signal control using a novel multiagent reinforcement learning［J］. IEEE Transactions on Cybernetics, 2020, 51(1): 174-187.
［27］ Su Zicheng, Chow A H, Zhong Renxin. Adaptive network traffic control with an integrated model-based and data-driven approach and a decentralised solution method［J］. Transportation Research Part C: Emerging Technologies, 2021, 128: 103154.
［28］ Tan Tian, Bao Feng, Deng Yue, et al. Cooperative deep reinforcement learning for large-scale traffic grid signal control［J］. IEEE Transactions on Cybernetics, 2020, 50(6): 2687-2700.
［29］ Ge Hongwei, Gao Dongwan, Sun Liang, et al. Multi-agent transfer reinforcement learning with multi-view encoder for adaptive traffic signal control［J］. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 12572-12587.
［30］ Li Li, Lv Yisheng, Wang Feiyue. Traffic signal timing via deep reinforcement learning［J］. IEEE/CAA Journal of Automatica Sinica, 2016, 3(3): 247-254.
［31］ Jiang Shan, Huang Yufei, Jafari M, et al. A distributed multi-agent reinforcement learning with graph decomposition approach for large-scale adaptive traffic signal control［J］. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 14689-14701.
［32］ Chu Tianshu, Wang Jie, Codeca L, et al. Multi-agent deep reinforcement learning for large-scale traffic signal control［J］. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(3): 1086-1095.
［33］ Li Zhenning, Yu Hao, Zhang Guohui, et al. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning［J］. Transportation Research Part C: Emerging Technologies, 2021, 125: 103059.
［34］ Nishi T, Otaki K, Hayakawa K, et al. Traffic signal control based on reinforcement learning with graph convolutional neural nets［C］//Proceedings of the 21st International Conference on Intelligent Transportation Systems, Maui, HI, November 4-7, 2018.
［35］ Wang Zixin, Zhu Hanyu, He Mingcheng, et al. GAN and multi-agent DRL based decentralized traffic light signal control［J］. IEEE Transactions on Vehicular Technology, 2022, 71(2): 1333-1348.
［36］ Dubois D J. Fuzzy sets and systems: theory and applications［M］. New York: Academic Press, 1980.
［37］ Mitchell M. An introduction to genetic algorithms［M］. Cambridge: MIT press, 1998.
［38］ Michalewicz Z. Genetic algorithms+data structures=evolution programs［M］. Berlin: Springer-Verlag, 1992.
［39］ Zhang Guohui, Wang Yinhai. Optimizing minimum and maximum green time settings for traffic actuated control at isolated intersections［J］. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 164-173.

[1]	曾庆华,宗刚. 城市多级公交线路优化设计研究[J]. 中国管理科学, 2024, 32(9): 121-130.
[2]	姚智胜, 邵春福, 熊志华. 基于小波包和最小二乘支持向量机的短时交通流组合预测方法研究[J]. 中国管理科学, 2007, 15(1): 64-68.

效率公平防溢并重的多交叉口协调控制

A Multi-intersection Coordinated Control with Equal Emphasis on Efficiency, Fairness, and Spillback

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

Metrics

本文评价

推荐阅读 0