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Traffic Jams Detection and Congestion Avoidance in Smart City Using Parallel K-Means Clustering Algorithm

  • Doreswamy
  • Osama A. Ghoneim
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 14)

Abstract

With the rapid development in the society, there is a huge advancement in transportation and communication sector. But, the transportation industry is also facing a lot of challenges. Big data can come to the rescue of this area as the huge amount of traffic data which is generated every day and everywhere inside a lot of smart cities is stored digitally and can be analyzed using various data mining techniques. So traffic control in smart city using data mining becomes a hot research area. In this paper a traffic control model has been presented. In this paper we have present parallel in-memory computing model using K-means clustering algorithm. The proposed model gives us a complete picture about the traffic condition inside the Aaruthu city and these results are updated every five minutes according to the data set which had been used in this model. This model makes the transportation in the city easier. As it helps the citizens to avoid the traffic jams in order to save their time.

Keywords

Big data Smart city Traffic jam Congestion avoidance Data mining K-means 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Computer Science DepartmentMangalore UniversityMangaloreIndia
  2. 2.Computer Science Division, Mathematics DepartmentTanta UniversityTantaEgypt

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