A framework for smart traffic management using hybrid clustering techniques

E. Vijay Sekar, J. Anuradha, Anshita Arya, Balamurugan Balusamy, Victor Chang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)347-362
Number of pages16
JournalCluster Computing
Issue number1
Publication statusPublished - 1 Mar 2018


  • Big data
  • Clustering
  • DBScan
  • Spatial data mining
  • Traffic management


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