Cluster Computing

, Volume 21, Issue 1, pp 347–362 | Cite as

A framework for smart traffic management using hybrid clustering techniques

  • E. Vijay Sekar
  • J. Anuradha
  • Anshita Arya
  • Balamurugan Balusamy
  • Victor ChangEmail author


Due to increase in traffic in cities and on major roads, it has become a necessity to have an efficient traffic management system to handle such scenarios. Present traffic management system performs mere traffic monitoring and event handling which cannot be a viable system for highly populous country like India and China. In this paper, we propose a system that will predict the densely populated roads based on the present and past traffic congestion. This system also suggests the alternate paths for the given source and destination. A simulation of live stream of online data is performed on legacy traffic data set which is processed incrementally. Density based clustering is applied after Fuzzification of data to assign weightage for the densely congested path on the route map. The weightage for the path on the given time helps to decide the best route form the source to destination. Floyd’s algorithm is also applied to find the shortest set of alternate path for the given source to destination.


Traffic management Spatial data mining Clustering DBScan Big data 


  1. 1.
    Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 81–92. VLDB Endowment (2003)Google Scholar
  2. 2.
    Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM Sigmod Rec. 28(2), 49–60 (1999)CrossRefGoogle Scholar
  3. 3.
    Anuradha, J., Tripathy, B.K.: An optimal rough fuzzy clustering algorithm using particle swarm optimization. Int. J. Data Min. Model. Manag. 7(4), 257–275 (2015)Google Scholar
  4. 4.
    Bäcklund, H., Hedblom, A., Neijman, N.: A density-based spatial clustering of application with noise. Data Min. TNM033, 11–30 (2011)Google Scholar
  5. 5.
    Bertini, R.L., El-Geneidy, A.: Advanced traffic management system data. In: Assessing the Benefits and Costs of ITS, pp. 287–314. Springer, Berlin (2004)Google Scholar
  6. 6.
    Bramer, M.A.: Knowledge discovery and data mining (no. 1) (1999)Google Scholar
  7. 6.
    Chakraborty, S., Nagwani, N.K.: Analysis and study of incremental DBSCAN clustering algorithm. arXiv:1406.4754 (2014)
  8. 7.
    Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: SDM, vol. 6, pp. 328–339 (2006)Google Scholar
  9. 8.
    Chang, V.: Towards data analysis for Weather Cloud Computing. Knowl. Based Syst. (2017, accepted)Google Scholar
  10. 9.
    Chen, Y., Tu, L.: Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM 2007Google Scholar
  11. 10.
    Diker, A.C., Nasibov, E.: Estimation of traffic congestion level via fn-dbscan algorithm by using GPS data. In: Problems of Cybernetics and Informatics (PCI), 2012 IV International Conference, pp. 1–4. IEEE (2012)Google Scholar
  12. 11.
    Erman, J., Arlitt, M., Mahanti, A.: Traffic classification using clustering algorithms. In: Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, pp. 281–286. ACM (2006)Google Scholar
  13. 12.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96(34), 226–231 (1996)Google Scholar
  14. 13.
    Ester, M., Kriegel, H.P., Sander, J., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. VLDB 98, 323–333 (1998)Google Scholar
  15. 14.
    Goyal, N., Goyal, P., Venkatramaiah, K., Deepak, P.C., Sanoop, P.S.: An efficient density based incremental clustering algorithm in data warehousing environment. In: 2009 International Conference on Computer Engineering and Applications IPCSIT, vol. 2. IACSIT Press, Singapore (2009)Google Scholar
  16. 15.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, New York (2011)zbMATHGoogle Scholar
  17. 16.
    Nagpal, P.B., Mann, P.A.: Comparative study of density based clustering algorithms. Int. J. Comput. Appl. 27(11), 421–435 (2011)Google Scholar
  18. 17.
    Schrank, D., Eisele, B., Lomax, T., Bak, J.: Mobility Report. Texas A&M Transportation Institute (2015)Google Scholar
  19. 18.
    Tan, P.N., Steinbach, M., Kumar, V.: Data mining cluster analysis: basic concepts and algorithms (2013)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • E. Vijay Sekar
    • 1
    • 3
  • J. Anuradha
    • 1
    • 3
  • Anshita Arya
    • 1
    • 3
  • Balamurugan Balusamy
    • 2
    • 3
  • Victor Chang
    • 3
    Email author
  1. 1.School of Computer Science and EngineeringVIT UniversityVelloreIndia
  2. 2.School of Information Technology and EngineeringVIT UniversityVelloreIndia
  3. 3.Xi’an Jiaotong-Liverpool UniversitySuzhouChina

Personalised recommendations