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

被引:25
|
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
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