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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
Article

Abstract

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.

Keywords

Traffic management Spatial data mining Clustering DBScan Big data 

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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

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