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Complexity
Volume 2017 (2017), Article ID 5067145, 11 pages
https://doi.org/10.1155/2017/5067145
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.

How to Cite this Article

Li Wang, Shimin Lin, Jingfeng Yang, et al., “Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm,” Complexity, vol. 2017, Article ID 5067145, 11 pages, 2017. doi:10.1155/2017/5067145