SHAN Zhenyu, PAN Zhigeng, LI Fengwei, XU Huihui, LI Jiming. Visual Analytics of Traffic Congestion Propagation Path with Large Scale Camera Data[J]. Chinese Journal of Electronics, 2018, 27(5): 934-941. doi: 10.1049/cje.2018.04.011
Citation: SHAN Zhenyu, PAN Zhigeng, LI Fengwei, XU Huihui, LI Jiming. Visual Analytics of Traffic Congestion Propagation Path with Large Scale Camera Data[J]. Chinese Journal of Electronics, 2018, 27(5): 934-941. doi: 10.1049/cje.2018.04.011

Visual Analytics of Traffic Congestion Propagation Path with Large Scale Camera Data

doi: 10.1049/cje.2018.04.011
Funds:  This work is supported by the Natural Science Foundation of Zhejiang poince (No.LY17F020017), the Natural Science Foundation of China (No.61304188), the Science and Technology Plan Project of Hangzhou (No.20160533B81), and the Key Laboratory Projects of Ministry of Public Security (No.2016DSJSYS004).
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  • Corresponding author: PAN Zhigeng (corresponding author) Director, Digital Media and HCI Research Center, Hangzhou Normal University. He got his Ph.D. degree in 1993, and he became a full professor in Zhejiang University in 1996 because of his excellent academic performance. He has published more than 100 technical papers on important journals (e.g. PAMI, TVCG, IEEE Multimedia, etc.) and conferences (e.g. ACM Multimedia, IEEE VR, etc.). He is a member of IEEE, ACM SIGGRAPH. His research interests include virtual reality, computer graphics and HCI. Currently, he is the editor-in-chief of Transactions on Edutainment. He is the program co-chair of CASA2011, SIGGRAPH Asia 2011, IEEE VR 2013, conference cochair of VRCAI2012/VRCAI2013/VRCAI2015/Edutainment2018. (Email:zgpan@cad.zju.edu.cn)
  • Received Date: 2017-03-16
  • Rev Recd Date: 2018-01-02
  • Publish Date: 2018-09-10
  • Congestion analysis is essential to traffic control, especially in crowded urban road network. The recent traffic forecasting methods can provide travelers and traffic managers with early congestion warning, yet unable to reveal the relationship of congestion roads. This paper presented a congestion propagation path estimation method based on greedy algorithm to quickly extract these congestion relationships for visual analytics. The data from traffic cameras are applied to build the propagation network based on a directed weighted graph. It describes the process of congestion spreading among different segments. According to this network, congestion propagation path predicts the process of congestion spreading between different segments. In our visual design, it is applied to demonstrate the segments that will be influenced by the congested road. This is helpful for traffic managers to make effective and efficient decisions. The experimental result shows that our method achieves high accuracy thus prove the effective for the congestion propagation method.
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