Iterative Tuning Strategy for Setting Phase Splits in Traffic Signal Control

被引:0
|
作者
Wang, Yu [1 ]
Wang, Danwei [1 ]
Xiao, Nan [2 ]
Li, Yitong [2 ]
Frazzoli, Emilio [3 ]
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, EXQUISITUS Ctr E City, Singapore 639798, Singapore
[2] Singapore MIT Alliance Res & Technol Ctr, Singapore 138602, Singapore
[3] MIT, Cambridge, MA 02139 USA
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper introduces Iterative Tuning (IT) strategy for urban traffic signal control. This strategy is motivated by people's daily repetitive travel patterns between homes and working places. Statistical analysis of a real traffic network shows that traffic flows of junctions are repetitive with small variations on a weekly basis. The main idea of IT is that, daily traffic signal schedules are tuned with anticipation of traffic demands. In this paper, only phase split is tuned iteratively to balance the traffic demands from all directions in a junction. Each junction has its own controller and these controllers can work cooperatively to improve the network performance after several iterations. Therefore IT strategy is scalable for arbitrary large urban networks. Marina Bay and Clementi areas in Singapore based on real traffic data are simulated and simulation results show that IT strategy can improve the performance considerably comparing with fixed-time strategy.
引用
收藏
页码:2453 / 2458
页数:6
相关论文
共 50 条
  • [31] A Traffic Signal Control Strategy to Avoid Spillback on Short Links
    Mohajerpoor, Reza
    Cai, Chen
    2020 AMERICAN CONTROL CONFERENCE (ACC), 2020, : 1167 - 1172
  • [32] An extended signal control strategy for urban network traffic flow
    Yan, Fei
    Tian, Fuli
    Shi, Zhongke
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2016, 445 : 117 - 127
  • [33] Traffic signal bus-priority control strategy and intelligent control method
    Kuang, Xian-Yan
    Xu, Lun-Hui
    Huang, Yan-Guo
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2012, 29 (10): : 1284 - 1290
  • [34] Genetic algorithm, local and iterative searches for combining traffic assignment and signal control
    Lee, C
    Machemehl, RB
    TRAFFIC AND TRANSPORTATION STUDIES, 1998, : 488 - 497
  • [35] PlanLight: Learning to Optimize Traffic Signal Control With Planning and Iterative Policy Improvement
    Zhang, Huichu
    Kafouros, Markos
    Yu, Yong
    IEEE ACCESS, 2020, 8 : 219244 - 219255
  • [36] Estimated Response Iterative Tuning with signal projection
    Fujimoto, Yusuke
    IFAC JOURNAL OF SYSTEMS AND CONTROL, 2022, 19
  • [37] LHOVRA - A NEW TRAFFIC SIGNAL CONTROL STRATEGY FOR ISOLATED JUNCTIONS.
    Peterson, Alf
    Bergh, Torsten
    Steen, Kenneth
    Traffic Engineering and Control, 1986, 27 (7-8): : 388 - 389
  • [38] An iterative approach to enhanced traffic signal optimization
    Wong, Y. K.
    Woon, W. L.
    EXPERT SYSTEMS WITH APPLICATIONS, 2008, 34 (04) : 2885 - 2890
  • [39] Auto-tuning of lead step in phase lead iterative learning control
    Wang Danwei
    Zhang Bin
    Ye Yongqang
    Zhang Xinzheng
    PROCEEDINGS OF THE 24TH CHINESE CONTROL CONFERENCE, VOLS 1 AND 2, 2005, : 618 - 623
  • [40] Design of Iterative Learning Control for a subclass of robotic systems with an alternative reference signal setting
    Hladowski, Lukasz
    Sulikowski, Bartlomiej
    Galkowski, Krzysztof
    2024 28TH INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN AUTOMATION AND ROBOTICS, MMAR 2024, 2024, : 270 - 275