山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (4): 48-55.doi: 10.6040/j.issn.1672-3961.0.2022.302
• 交通工程——智慧交通专题 • 上一篇
周晓昕,廖祝华*,刘毅志,赵肄江,方艺洁
ZHOU Xiaoxin, LIAO Zhuhua*, LIU Yizhi, ZHAO Yijiang, FANG Yijie
摘要： 针对当前信号灯配时方法应对低高峰期车流量与方向的频繁变换困难问题,提出一种融合历史与当前交通流量的交通信号控制方法。提出基于历史交通数据的路口各方向车流量的规律预测模型、规律与实时车流量学习的融合模型、各相位信号灯最佳信号周期和绿灯时间估计算法。使用真实道路交通数据在交通仿真模拟器(simulation of urban mobility, SUMO)进行仿真试验,结果表明:与定时控制、模糊控制等方法对比,本研究提出的方法减少了车辆的等待时间与等待队伍的长度;通过多个不同类型路口信号灯的综合分析,能够整体提高城市道路出行与服务质量,提升交通运行效率。
中图分类号:
| [1] LIAO Z H, XIAO H. Impact assessing of traffic lights via GPS vehicle trajectories[J]. ISPRS International Journal of Geo-Information, 2021, 10(11): 769-782. [2] SOUA R, KOESDWIADY A, KARRAY F. Big-data-generated traffic flow prediction using deep learning and dempster shafer theory[C] //International Joint Conference on Neural Networks. Vancouver, Canada: IEEE, 2016: 3195-3202. [3] KOESDWIADY A, SOUA R, KARRAY F. Improving traffic flow prediction with weather information in connectedcars: a deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2016, 65(12): 9508-9517. [4] JIA Y, WU J, XU M. Traffic flow prediction with rainfall impact using a deep learning method[J]. Journal of Advanced Transportation, 2017(722):1-10. [5] FU R, ZHANG Z, LI L. Using lstm and gru neural network methods for traffic flow prediction [C] // Youth Academic Annual Conference of Chinese Association of Automation(YAC). Wuhan, China, 2017: 324-328. [6] ZHAO L, SONG Y, ZHANG C, et al. T-GCN: a temporal graph convolutional network for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(9): 3848-3858. [7] 吴昊昇,郑皎凌,王茂帆. TR-light:基于多信号灯强化学习的交通组织方案优化算法[J]. 计算机应用研究,2022,39(2): 504-509. WU Haosheng, ZHENG Jiaoling, WANG Maofan. TR-light: traffic organization scheme optimization algorithm based on multi signal reinforcement learning[J] Computer Application Research, 2022, 39(2):504-509. [8] HERNANDEZ P, KARTAL B, TAYLOR M E. A survey and critique of multiagent deep reinforcement learning[J]. Autonomous Agents and Multi-Agent Systems, 2019, 33(6): 750-797. [9] ALI M E M, DURDE A, CELTEK S A, et al. An adaptive method for traffic signal control based on fuzzy logic with webster and modified webster formula using SUMO traffic simulator[J]. IEEE Access, 2021, 9: 102985-102997. [10] GU J, FANG Y, SHENG Z, et al. Double deep Q-network with a dual-agent for traffic signal control[J]. Applied Sciences, 2020, 10(5):1622. [11] VAN H, GUEZ A, SILVER D. Deep reinforcement learning with double q-learning[C] // Proceedings of the AAAI Conference on Artificial Intelligence. Austin, USA:[s.n.] , 2016, 30(1). [12] WEI H, CHEN CC, ZHENG G J, et al. Presslight: learning mar pressure control to coordinate traffic signals in arterial network[C] //Proc of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2019: 1290-1298. [13] ZAIED A N H, Al O W. Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait[J]. Expert Systems with Applications, 2011, 38(8): 9434-9441. [14] YAGER R R, ZADEH L A. An introduction to fuzzy logic applications in intelligent systems[M]. Boston: Springer Science & Business Media, 2012: 87-95. [15] JIANG T, WANG Z, CHEN F. Urban traffic signals timing at four-phase signalized intersection based on optimized two-stage fuzzy control scheme[J]. Mathematical Problems in Engineering, 2021, 2(1): 1-9. [16] ZHENG F F, LIU H J, et al. Reliability-based traffic signal control for urban arterial roads[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(3): 643-655. [17] 陈兆盟,刘小明,吴文祥,等.结合信号控制的交通状态及其真实性判别方法[J].重庆交通大学学报(自然科学版),2016,35(6):95-100. CHEN Zhaomeng, LIU Xiaoming, WU Wenxiang, et al. Identification method of traffic state and its authenticity combined with signal control[J]. Journal of Chongqing Jiaotong University(Natural Science Edition), 2016, 35(6): 95-100. [18] 孔军伟.基于车辆排队长度的城市道路交通流宏观基本图研究[D].北京:北京交通大学,2019. KONG Junwei. Vehicle queue-length-based urban road traffic flow macroscopic fundamental diagrams[D]. Beijing: Beijing Jiaotong University, 2019. [19] 蒋欢昕,王涛,程一一,等.基于多指标融合的单交叉口运行状态实时评价方法[J]. 交通信息与安全,2020,38(4): 84-94. JIANG Huanxin, WANG Tao, CHENG Yiyi, et al. A real time operational state evaluation method for isolated intersections based on multi-index fusion[J]. Journal of Transport Information and Safety, 2020, 38(4): 84-94. [20] 批准部门浙江省建设厅. 城市道路人行过街设施规划与设计规范[S]. 浙江工商大学出版社, 2009:14-15. |
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