What Is the Root Cause of Congestion in Urban Traffic Networks: Road Infrastructure or Signal Control?

被引:22
|
作者
Yue, Wenwei [1 ,2 ]
Li, Changle [1 ,2 ]
Chen, Yue [1 ,2 ]
Duan, Peibo [3 ]
Mao, Guoqiang [1 ,2 ]
机构
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Xidian Univ, Res Inst Smart Transportat, Xian 710071, Peoples R China
[3] Northeastern Univ, Sch Software, Shenyang 110819, Peoples R China
基金
中国国家自然科学基金;
关键词
Congestion root cause identification; road infrastructure; signal control; congestion propagation; gradient boasting decision tree; SUMO; ALGORITHM;
D O I
10.1109/TITS.2021.3085021
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Identifying the root cause of congestion and taking appropriate strategies to improve traffic network performance are important goals of Advanced Traffic Management Systems (ATMS). On many occasions, the causes of congestion are not necessarily attributable to road infrastructures themselves. Instead, signal control strategies at intersections are very often the major contributors of congestion. In lieu of this, in this paper, a root cause identification method is developed with consideration of the impact from both road infrastructure and traffic signal control. Firstly, we differentiate congestion effects between road segments and intersections to attribute the causes of congestion to road infrastructure and signal control respectively. Then, we construct causal congestion trees to model congestion propagation and quantify congestion costs for each road segment and intersection in the whole road network. A Markov model is utilized to capture congestion spatio-temporal correlation among multiple road segments and intersections simultaneously, with which the most critical root cause can be located. Furthermore, a gradient boosting decision tree based method is presented to predict the root cause of congestion according to traffic flows, signal control strategies and road topology in traffic networks. Finally, simulations based on Simulation of Urban Mobility (SUMO) validate the effectiveness of our proposed method in identifying and predicting the congestion root cause. Experiments are further conducted using inductive loop detector data to identify the root cause for the road network of Taipei.
引用
收藏
页码:8662 / 8679
页数:18
相关论文
共 50 条
  • [1] Neuro-Adaptive Traffic Congestion Control for Urban Road Networks
    Bechlioulis, Charalampos P.
    Kyriakopoulos, Kostas J.
    [J]. 2018 EUROPEAN CONTROL CONFERENCE (ECC), 2018, : 1685 - 1690
  • [2] Iterative learning approach for traffic signal control of urban road networks
    Yan, Fei
    Tian, Fuli
    Shi, Zhongke
    [J]. IET CONTROL THEORY AND APPLICATIONS, 2017, 11 (04): : 466 - 475
  • [3] A prediction model for traffic congestion in complex urban road networks
    Liu Z.
    Li J.
    Wang C.
    Cai S.-M.
    Tang M.
    Huang Q.
    Chen Z.-H.
    [J]. 1600, Univ. of Electronic Science and Technology of China (45): : 17 - 25
  • [4] Determinants of the congestion caused by a traffic accident in urban road networks
    Zheng, Zhenjie
    Wang, Zhengli
    Zhu, Liyun
    Jiang, Hai
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2020, 136 (136):
  • [5] Augmenting Traffic Signal Control Systems for Urban Road Networks With Connected Vehicles
    Rafter, Craig B.
    Anvari, Bani
    Box, Simon
    Cherrett, Tom
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 21 (04) : 1728 - 1740
  • [6] Diagnosis and Prediction of Traffic Congestion on Urban Road Networks Using Bayesian Networks
    Kim, Jiwon
    Wang, Guangxing
    [J]. TRANSPORTATION RESEARCH RECORD, 2016, (2595) : 108 - 118
  • [7] Model-Based Traffic Congestion Control in Urban Road Networks Analysis of Performance Criteria
    Lin, Shu
    Zhou, Zhao
    Xi, Yugeng
    [J]. TRANSPORTATION RESEARCH RECORD, 2013, (2390) : 112 - 120
  • [8] Robust Control for Urban Road Traffic Networks
    Tettamanti, Tamas
    Luspay, Tamas
    Kulcsar, Balzas
    Peni, Tamas
    Varga, Istvan
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2014, 15 (01) : 385 - 398
  • [9] Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations
    Zhang, Sen
    Li, Shaobo
    Li, Xiang
    Yao, Yong
    [J]. ALGORITHMS, 2020, 13 (04)
  • [10] Optimal Traffic Signal Control for an Urban Arterial Road
    Li Yinfei
    Wei Wei
    Chen, Shuping
    [J]. 2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL III, PROCEEDINGS, 2008, : 570 - +