Urban Traffic Congestion Detection Based on Clustering Analysis of Real-time Traffic Data

  • 1. Faculty of Computer Science and Technology, Southwest University for Nationalities, Chengdu 610041, China;
    2. Faculty of Geosciences and Environment Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received date: 2012-11-26

  Revised date: 2012-12-03

  Online published: 2012-12-25


Traffic congestion in urban road network heavily restricts transportation efficiency. Detecting traffic congestions in the spatio-temporal sense and identifying network bottlenecks become an important task in transportation management. Up to now, many traffic congestion detection methods have been proposed, which have focused on the detection of momentary local congestions. Larger-scale, longer-time and regular congestions can't be detected using these methods. That is because congestions have different temporo-spatial scales, and a characteristic is not considered in those methods. This paper proposes a new kind of urban traffic congestion detection method that deals with spatio-temporal extension of congestion. It is based on spatio-temporal clustering analysis of real-time traffic data. By defining a proper spatio-temporal correlation, the classic DBSCAN algorithm is adapted to tackle spatio-temporal clustering. With it we can detect longer time and regular traffic congestion in the spatio-temporal sense. Experiments have been conducted using real traffic condition data of Chengdu to validate the effectiveness of the method. The experiment shows that the proposed method can detect the congestion areas and identify the spatio-temporal extent of congestions accurately. The detected congestion areas were compared with congestion report from local traffic management authority and found to be consistent with the later.

Cite this article

LU Xiao-Ya, SONG Zhi-Hao, XU Zhu, LI Mu-Zi, LI Ting, SUN Wei-E . Urban Traffic Congestion Detection Based on Clustering Analysis of Real-time Traffic Data[J]. Journal of Geo-information Science, 2012 , 14(6) : 775 -780 . DOI: 10.3724/SP.J.1047.2012.00775


[1] Jiang G Y, Niu S F, An D C. The method of traffic congestion identification and spatial and temporal dispersion range estimation [C]. International Asia Conference on Informatics in Control, Automation and Robotics, 2010,1:36-39.

[2] 祝付玲.城市道路交通拥堵评价指标体系研究 [D].南京:东南大学,2006.

[3] 赵有婷,李熙莹,等. 基于视频全局光流场的交通拥堵检测[J].计算机应用研究, 2010, 27(11):4355-4362.

[4] Li L, Chen L, Huang X, Huang J. A traffic congestion estimation approach from video using time-spatial imagery [C]. ICINIS '08 Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent Systems, 2008, 465-469.

[5] 石征华,侯忠生.城市快速路拥挤度判别方法研究[J].交通与计算机,2006,24(5):20-23.

[6] Payne H and Knoel H. Development and testing of incident-detection algorithm[J]. research methodology and detailed results [R]. FHWA-RD-06-20, 1976.

[7] 刘伟铭.高速公路系统控制方法[M].北京:人民交通出版社,1998.

[8] 周成虎,裴涛.地理信息系统空间分析原理[M].北京:科学出版社,2011.

[9] Michalopoulos P G. Vehicle detection video through image processing:The autoscope system[J].IEEE Trans on Vehicular Technology,1991, 40(1):21-29.

[10] Yang Y, Cui Z, Wu J. Fuzzy c-means clustering and opposition-based reinforcement learning for traffic congestion identification[J]. Journal of Information and Computational Science, 2012, 9(9): 2441-2450.

[11] Lozano A, Manfredi G, Luciano N. An algorithm for the recognition of levels of congestion in road traffic problems[J]. Mathematics and Computers in Simulation,2009,79(6):1926-1934.

[12] Erman J, Arlitt M F, Mahanti A. Traffic classification using clustering algorithms [C]. Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, MineNet'06, 2006.

[13] 贾森. 基于实时信息的城市道路交通状态判别方法研究 [D].北京:北京交通大学,2007.

[14] Ester M, Kriegel H, Sander J, and Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise [C]. KDD-96 Proceedings,1996,226-231.

[15] 吴信才,曹志月.时态GIS的基本概念、功能及实现方法[J].地球科学—中国地质大学学报,2002,27(3):241-245.

[16] Birant D and Kut A. ST-DBSCAN: An algorithm for clustering spatial-temporal data[J]. Data and Knowledge Engineering, 2007, 60(1):208-221.

[17] 邓敏,刘启亮,王佳,等.时空聚类分析的普适性方法[J].中国科学:信息科学, 2012, 42(1):111-124.