Detecting Congestion Patterns in Spatio Temporal Traffic Data using Frequent Pattern Mining

International Journal of Computer Science and Engineering
© 2017 by SSRG - IJCSE Journal
Volume 4 Issue 10
Year of Publication : 2017
Authors : D.Gokula priya, Dr.C.Saravanabhavan, K.Suvitha

How to Cite?

D.Gokula priya, Dr.C.Saravanabhavan, K.Suvitha, "Detecting Congestion Patterns in Spatio Temporal Traffic Data using Frequent Pattern Mining," SSRG International Journal of Computer Science and Engineering , vol. 4,  no. 10, pp. 11-14, 2017. Crossref,


Data mining is a process of extracting valuable and invaluable information from the large data base. Congestion on road is the condition in which it is characterized as slow speed and long travel time. The detection of unusual traffic patterns is an important research problem in the data mining. In this research, the detection of unusual traffic patterns based on spatio-temporal traffic data is by constructing causal congested tree and then to find the frequent sub tree, FP-Growth algorithm is used. Frequent substructures of these causality trees reveal not only recurring interactions among spatial-temporal congestions, but potential bottlenecks or flaws in the design of existing traffic networks. The FP-Growth algorithm is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree.


Spatio-temporal, FP-Growth, Frequent Pattern.


1. A. C. Lozano, et al., “Spatial-temporal causal modeling for climate change attribution,” in Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2009, pp. 587–596.
2. E. H.-C. Lu, W.-C. Lee, and V. S. Tseng, “Mining fastest path from trajectories with multiple destinations in road networks,” Knowl. Inf. Syst., vol. 29, no. 1, pp. 25–53, 2011.
3. L. X. Pang, S. Chawla, W. Liu, and Y. Zheng, “On detection of emerging anomalous traffic patterns using GPS data,” Data Knowl. Eng., vol. 87, pp. 357–373, 2013.
4. H.-S. Qi, D.-H. Wang, and P. Chen, “Formation and propagation of local traffic jam,” Discrete Dynamics Nature Soc., vol. 2013, 2013, Art. no. 748529.
5. C. Renso, M. Baglioni, J. A. F. de Macedo, R. Trasarti, and M. Wachowicz, “How you move reveals who you are: Understanding human behavior by analyzing trajectory data,” Knowl. Inf. Syst., vol. 37, pp. 331–362, 2012.
6. X. Wang, G. Li, G. Jiang, and Z. Shi, “Semantic trajectorybased event detection and event pattern mining,” Knowl. Inf. Syst., vol. 37, pp. 305–329, 2011.
7. C. Zhang, S. Sun, and G. Yu, “A Bayesian network approach to time series forecasting of short-term traffic flows,” in Proc. 7th Int. IEEE Conf. Intell. Transp. Syst., 2004, pp. 216–221.
8. A. Hofleitner, R. Herring, P. Abbeel, and A. Bayen, “Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 4, pp. 1697–1693, Dec. 2012.
9. Y. Zheng, L. Capra, O. Wolfson, and H. Yang, “Urban computing: Concepts, methodologies, and applications,” ACM Trans. Intell. Syst. Technol., vol. 5, no. 3, 2014, Art. no. 38. 
10. B. Zhang, C. Zhang, and X. Yi, “Competitive EM algorithm for finite mixture models,” Pattern Recog., vol. 37, no. 1, pp. 131–144, 2004.
11. Hoang Nguyen , Wei Liu and Fang Chen, “Discovering congestion propagation in spatio-temporal traffic data”, IEEE Trans. On Big Data , vol.3 , No.2,April-June 2017