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
  • [21] An iterative learning approach for anticipatory traffic signal control on urban networks
    Huang, Wei
    Viti, Francesco
    Tampere, Chris M. J.
    TRANSPORTMETRICA B-TRANSPORT DYNAMICS, 2017, 5 (04) : 402 - 425
  • [22] Iterative learning approach for traffic signal control of urban road networks
    Yan, Fei
    Tian, Fuli
    Shi, Zhongke
    IET CONTROL THEORY AND APPLICATIONS, 2017, 11 (04): : 466 - 475
  • [23] Traffic signal hybrid control method based on iterative learning and model predictive control
    Yan F.
    Li P.
    Xu X.-Y.
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2021, 38 (03): : 339 - 348
  • [24] Optimizing green splits in high-dimensional traffic signal control with trust region Bayesian optimization
    Gong, Yunhai
    Zhong, Shaopeng
    Zhao, Shengchuan
    Xiao, Feng
    Wang, Wenwen
    Jiang, Yu
    COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2025, 40 (06) : 741 - 763
  • [25] Feedforward Tuning by Fitting Iterative Learning Control Signal for Precision Motion Systems
    Dai, Luyao
    Li, Xin
    Zhu, Yu
    Zhang, Ming
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2021, 68 (09) : 8412 - 8421
  • [26] Signal Processing Aspects in State Feedback Control Based on Iterative Feedback Tuning
    Radac, Mircea-Bogdan
    Precup, Radu-Emil
    Preitl, Stefan
    Petriu, Emil M.
    Dragos, Claudia-Adina
    Paul, Adrian Sebastian
    Kilyeni, Stefan
    HSI: 2009 2ND CONFERENCE ON HUMAN SYSTEM INTERACTIONS, 2009, : 37 - 42
  • [27] Implementation of the OPAL adaptive control strategy in a traffic signal network
    Gartner, NH
    Pooran, FJ
    Andrews, CM
    2001 IEEE INTELLIGENT TRANSPORTATION SYSTEMS - PROCEEDINGS, 2001, : 195 - 200
  • [28] An approximate dynamic programming strategy for responsive traffic signal control
    Cai, Chen
    2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, 2007, : 303 - 310
  • [29] A Regional Traffic Signal Control Strategy with Deep Reinforcement Learning
    Li, Congcong
    Yan, Fei
    Zhou, Yiduo
    Wu, Jia
    Wang, Xiaomin
    2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 7690 - 7695
  • [30] OPAC: A demand-responsive strategy for traffic signal control
    Gartner, Nathan H.
    Transportation Research Record, 1983, 1983 (906) : 75 - 81