Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm
Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of tr...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/5067145 |
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| author | Li Wang Shimin Lin Jingfeng Yang Nanfeng Zhang Ji Yang Yong Li Handong Zhou Feng Yang Zhifu Li |
| author_facet | Li Wang Shimin Lin Jingfeng Yang Nanfeng Zhang Ji Yang Yong Li Handong Zhou Feng Yang Zhifu Li |
| author_sort | Li Wang |
| collection | DOAJ |
| description | Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole. |
| format | Article |
| id | doaj-art-8ae6d56506d4438ab64e065f37792bad |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8ae6d56506d4438ab64e065f37792bad2025-02-03T06:47:55ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/50671455067145Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration AlgorithmLi Wang0Shimin Lin1Jingfeng Yang2Nanfeng Zhang3Ji Yang4Yong Li5Handong Zhou6Feng Yang7Zhifu Li8School of Computer Software, Tianjin University, Tianjin 300072, ChinaDepartment of Computer Science and Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Shatin 999077, Hong KongOpen Laboratory of Geo-Spatial Information Technology and Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, ChinaGuangzhou Entry-Exit Inspection and Quarantine Bureau, Guangzhou 510623, ChinaOpen Laboratory of Geo-Spatial Information Technology and Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, ChinaOpen Laboratory of Geo-Spatial Information Technology and Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, ChinaGuangzhou Yuntu Information Technology Co., Ltd., Guangzhou 510665, ChinaDepartment of Computer Science and Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Shatin 999077, Hong KongSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, ChinaTraffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.http://dx.doi.org/10.1155/2017/5067145 |
| spellingShingle | Li Wang Shimin Lin Jingfeng Yang Nanfeng Zhang Ji Yang Yong Li Handong Zhou Feng Yang Zhifu Li Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm Complexity |
| title | Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm |
| title_full | Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm |
| title_fullStr | Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm |
| title_full_unstemmed | Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm |
| title_short | Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm |
| title_sort | dynamic traffic congestion simulation and dissipation control based on traffic flow theory model and neural network data calibration algorithm |
| url | http://dx.doi.org/10.1155/2017/5067145 |
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