Trajectory Data-Driven Pattern Recognition of Congestion Propagation in Road Networks
The congestion pattern recognition in urban road networks helps for recognizing the bottleneck in road networks and assisting to route planning. With the widespread use of GPS devises in vehicles, it is possible for researchers to monitor the traffic condition of urban transport networks at a road level. In this paper, we utilize the trajectory data of vehicle GPS to detect the road travel speed by matching points of trajectories to road segments. A fuzzy clustering based method is proposed to classify the road congestion level according to the road traffic conditions. Further, the road network is clustered by the proposed snake clustering algorithm, so that the road network is divided into congested and uncongested areas. This paper studies the congestion propagation problem and propose to employ the dynamic Bayesian network for modeling the congestion propagation process. Taking the real road network of Shanghai and the dataset of GPS trajectories generated by more than 10,000 taxis, we evaluate the pattern recognition based congestion prediction method. It shows that the proposed model outperforms the competing baselines.
KeywordsCongestion propagation Taxi trajectories Road network Dynamic bayesian network
We thanks that this work was financially supported by National Natural Science Foundation of China (61772230, 61702215), Science & Technology Development Project of Jilin Province (20160204021GX) and Special Foundation Project for Industrial Innovation of Jilin Province (2017C032-1).
- 1.Jiang, G.: Link Dividing method for traffic information collecting based on GPS equipped floating car. Geomat. Inf. Sci. Wuhan Univ. 35(1), 41–42 (2010)Google Scholar
- 2.Kristensen, J.P., Nielsen, O.A.: Measuring congestion in Copenhagen with gps. Leopoldo Abad Alcalá 61(2), págs. 17–48 (2006)Google Scholar
- 3.Chang, A., Jiang, G., Niu, S.: Traffic congestion identification method based on GPS equipped floating car. In: International Conference on Intelligent Computation Technology and Automation, pp. 1069–1071. IEEE Computer Society (2010)Google Scholar
- 5.Holm, J.: Key performance indicators for congestion using GPS data. In: 19th ITS World Congress (2012)Google Scholar
- 6.Arnott R, Small K. The Economics of Traffic Congestion[J]. American Scientist, 1994, 82(5):446–455Google Scholar
- 8.Xu X, Gao X, Zhao X, et al. A novel algorithm for urban traffic congestion detection based on GPS data compression[C]// IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2016:107–112Google Scholar
- 10.Nielsen, O.A.: Analysis of congestion and speeds based on GPS-data. Traffic Days Auc (2003) Google Scholar
- 12.Anwar, T., Liu, C., Hai, L.V., et al.: Capturing the Spatiotemporal Evolution in Road Traffic Networks. IEEE Trans. Knowl. Data Eng. (2018)Google Scholar
- 19.Huang, W., Haiying, L.I., Wang, Y.: Passenger congestion propagation and control in peak hours for urban rail transit line. J. Railw. Sci. Eng. (2017)Google Scholar
- 20.Gao, Z.Y., Long, J.C., Li, X.G.: Congestion propagation law and dissipation control strategies for urban traffic. J. Univ. Shanghai Sci. Technol. 33(6), 701–708 (2011)Google Scholar