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Volume 2017 (2017), Article ID 5067145, 11 pages
Research Article

Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

1School of Computer Software, Tianjin University, Tianjin 300072, China
2Department of Computer Science and Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Shatin 999077, Hong Kong
3Open Laboratory of Geo-Spatial Information Technology and Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China
4Guangzhou Yuntu Information Technology Co., Ltd., Guangzhou 510665, China
5Guangzhou Entry-Exit Inspection and Quarantine Bureau, Guangzhou 510623, China
6School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Correspondence should be addressed to Jingfeng Yang and Zhifu Li

Received 30 June 2017; Accepted 16 October 2017; Published 31 December 2017

Academic Editor: Junpei Zhong

Copyright © 2017 Li Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.