Multicause Automatic Real-time Identification of Urban Road Traffic Congestion Based on Bayesian Network
曹堉1, 王成1, 杨岳铭1, 徐江涛1, 高悦尔2
1. School of Computer Science and Technology, Huaqiao University, Xiamen Fujian 361021, China; 2. School of Architecture, Huaqiao University, Xiamen Fujian 361021, China
Multicause Automatic Real-time Identification of Urban Road Traffic Congestion Based on Bayesian Network
CAO Yu1, WANG Cheng1, YANG Yue-ming1, XU Jiang-tao1, GAO Yue-er2
1. School of Computer Science and Technology, Huaqiao University, Xiamen Fujian 361021, China; 2. School of Architecture, Huaqiao University, Xiamen Fujian 361021, China
摘要Urban road traffic congestion is caused by various factors, such as dynamic and real-time, complexity, and changeability, and existing methods are subjective, have low accuracy and poor real-time, and unable to automatically identify. This study proposes a multicause automatic real-time identification of urban road traffic congestion on the basis of the Bayesian network. First, the method performs systematic mechanism analysis and simulation verification on the relationship between the dynamic observable variables of urban road traffic and the multiple congestion causes to construct the Bayesian network structure. Second, the obtained measured historical data are used for parameter learning and training to obtain the complete Bayesian network model. Finally, multiple causes of traffic congestion can be automatically and simultaneously identified in real time when inputting the observable variables of traffic under road working conditions into the Bayesian network model. This method has high flexibility that can help express the correlation of nodes better and has strong interpretability, which can fully use expert experience and knowledge and achieve automatization in real time. The results in the research on Quanxiu Street, Quanzhou City show that the construction of the multicause automatic real-time identification of urban road traffic congestion based on the Bayesian network is reasonable. Five congestion causes have higher recognition accuracy rates than contrast methods, which include pedestrian influence, peak traffic, parking occupied road, unreasonable signal timing, and impact of traffic crossing the road.
Abstract：Urban road traffic congestion is caused by various factors, such as dynamic and real-time, complexity, and changeability, and existing methods are subjective, have low accuracy and poor real-time, and unable to automatically identify. This study proposes a multicause automatic real-time identification of urban road traffic congestion on the basis of the Bayesian network. First, the method performs systematic mechanism analysis and simulation verification on the relationship between the dynamic observable variables of urban road traffic and the multiple congestion causes to construct the Bayesian network structure. Second, the obtained measured historical data are used for parameter learning and training to obtain the complete Bayesian network model. Finally, multiple causes of traffic congestion can be automatically and simultaneously identified in real time when inputting the observable variables of traffic under road working conditions into the Bayesian network model. This method has high flexibility that can help express the correlation of nodes better and has strong interpretability, which can fully use expert experience and knowledge and achieve automatization in real time. The results in the research on Quanxiu Street, Quanzhou City show that the construction of the multicause automatic real-time identification of urban road traffic congestion based on the Bayesian network is reasonable. Five congestion causes have higher recognition accuracy rates than contrast methods, which include pedestrian influence, peak traffic, parking occupied road, unreasonable signal timing, and impact of traffic crossing the road.
基金资助:Supported by the National Natural Science Foundation of China(No.51608209);the Surface Program of Natural Science Foundation of China (No.2017J01090);the Science and Technology Program of Quanzhou(No.2018Z008);the Postgraduate Research and Innovation Ability Cultivation Project of Huaqiao University(No.17014083001);the Project of Guiding Plan of Fujian Province(No.2019H0017)
通讯作者:
CAO Yu
E-mail: caoyu0701@foxmail.com
引用本文:
曹堉, 王成, 杨岳铭, 徐江涛, 高悦尔. Multicause Automatic Real-time Identification of Urban Road Traffic Congestion Based on Bayesian Network[J]. Journal of Highway and Transportation Research and Development, 2020, 14(4): 70-80.
CAO Yu, WANG Cheng, YANG Yue-ming, XU Jiang-tao, GAO Yue-er. Multicause Automatic Real-time Identification of Urban Road Traffic Congestion Based on Bayesian Network. Journal of Highway and Transportation Research and Development, 2020, 14(4): 70-80.
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2020, Vol. 14 
