|
|
|
| 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 |
|
|
|
|
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.
|
|
Received: 11 June 2020
|
| Fund: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) |
|
Corresponding Authors:
CAO Yu
E-mail: caoyu0701@foxmail.com
|
|
|
|
[1] LI Yu-xuan, WEI Ling-xiang, CHEN Hong, et al. Traffic Congestion Pattern Recognition in Urban Expressway Based on Support Vector Machine[J]. Journal of Transportation Engineering, 2018,18(1):43-47. (in Chinese)
[2] LI Gui-yi, HU Ming-hua, ZHENG Zhe. Multi-sector Traffic Congestion Identification Method Based on FCM-rough Sets[J]. Journal of Transportation Systems Engineering and Information Technology, 2017,17(6):141-146. (in Chinese)
[3] MENELAOU C, KOLIOS P, TIMOTHEHOU S, et al. Controlling Road Congestion via a Low-complexity Route Reservation Approach[J]. Transportation Research Part C:Emerging Technologies, 2017,81:118-136.
[4] MA Ya-feng. Research on Regional Traffic Congestion Measurement and Control Based on Inflow and Outflow Rate Matching[D]. Chengdu:Southwest Jiaotong University, 2018. (in Chinese)
[5] AN Meng, ZHU Ye, YANG Qiang, et al. Traffic Congestion Management Research Basedon Coordinated Control:A Case Study in Chongqing[J]. Journal of Transportation Engineering, 2018, 18(5):46-52, 64. (in Chinese)
[6] WANG Xiao, WANG Wu-hong, MAO Yan, et al. Fault Tree Analysis of Urban Traffic Congestion Causes[J]. Journal of Transport Information and Safety, 2012, 30(2):127-130. (in Chinese)
[7] WANG F Y, YE C M, LI Y. Study on Reasons and Countermeasures of Traffic Congestion of Urban Road Based on the Queuing Models[C]//2013 International Conference on Computational and Information Sciences. Shiyan:IEEE, 2013:1855-1858.
[8] MONDAL M, REHENA Z. Identifying Traffic Congestion Pattern Using K-means Clustering Technique[C]//20194th International Conference on Internet of Things:Smart Innovation and Usages, Ghaziabad. India:IEEE, 2019:1-5.
[9] XU H G, ZHANG H Y, ZONG F. Bayesian Network-Based Road Traffic Accident Causality Analysis[C]//2010 WASE International Conference on Information Engineering. Beidaihe:IEEE, 2010:413-417.
[10] ZHAO Jin-bao, DENG Wei, WANG Jian. Bayesian Network-based Urban Road Traffic Accidents Analysis[J]. Journal of Southeast University:Natural Science Edition, 2011, 41(6):1300-1306. (in Chinese)
[11] KUZMINSKI P, EISELE J S, GARBER N, et al. Improvement of Highway Safety I:Identification of Causal Factors through Fault-tree Modeling[J]. Risk Analysis, 1995,15(3):293-312.
[12] WANG Jing-yu. Research on Several Types of Probability Graph Model Structure Learning Algorithms[D]. Xi'an:Xidian University,2018. (in Chinese)
[13] YU Ping-ting, LIU Zhen-yuan, CHEN Xue-guang. A Bayesian Network Approach to Accident Analysis[J]. Journal of Safety Science and Technology, 2006, 2(4):45-50. (in Chinese)
[14] ZHANG Heng, XU Yin-feng, WU Teng-yu. Research on Traffic Delay Caused by Pedestrians' Group Psychology[J]. Journal of Systems Engineering,2016,31(4):536-544. (in Chinese)
[15] CUI Man, XUE Hui-feng, CHEN Fu-zhen, et al. Simulation of Traffic Congestion Prediction for Pedestrian Crossing Road[J].Computer Simulation,2018,35(2):81-84. (in Chinese)
[16] SUN Ya-nan. Analysis of Vehicle Running Rule in Road Traffic Congestion Environment[D].Kunming:Kunming University of Science and Technology,2016. (in Chinese)
[17] FENG Wei. Study on Influence of Roadside Parking on Traffic Flow[D]. Beijing:Beijing Jiaotong University, 2008. (in Chinese)
[18] LI Z F, ELEFTERIADOU L, RANKA S. Signal Control Optimization for Automated Vehicles at Isolated Signalized Intersections[J]. Transportation Research Part C:Emerging Technologies,2017,49:1-18.
[19] YU Min-jian. Study on Interaction between Traffic Flows of Signalless T-shaped Highway Intersection and Main Road[D]. Nanjing:Southeast University,2015. (in Chinese) |
|
|
|
2020, Vol. 14 
