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

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Proceedings of International Conference on Cognition and Recognition

Part of the book series: Lecture Notes in Networks and Systems ((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.

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References

  1. Djahel S, Doolan R, Muntean GM, Murphy J. A communications-oriented perspective on traffic management systems for smart cities: challenges and innovative approaches. IEEE Commun Surv Tutorials doi:10.1109/COMST.2014.2339817

  2. Zhang J, Wang F-Y et al (2011) Data-driven intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 12(4):1624–1639

    Article  Google Scholar 

  3. Lai C-F, Huang Y-M et al (2010) Adaptive body posture analysis using collaborative multi-sensors for elderly falling detection. IEEE Intell Syst 25(2):20–30

    Article  Google Scholar 

  4. Zhu G, Chen J, Zhang P (2015) Fuzzy c-means clustering identification method of urban road traffic state. In: 2015 12th international conference on fuzzy systems and knowledge discovery (FSKD), Zhangjiajie, pp 302–307

    Google Scholar 

  5. Huang Y, Sheng H, Chen J (2014) Intelligent congestion avoidance algorithm and system—application of data vitalization. In: 2014 14th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid), Chicago, IL, pp 847–856

    Google Scholar 

  6. Gecchele G, Rossi R, Gastaldi M, Caprini A (2011) Data mining methods for traffic monitoring data analysis: a case study. Procedia Soc Behav Sci 20:455–464. ISSN 1877-0428

    Google Scholar 

  7. Zamani Z, Pourmand M, Saraee MH (2010) Application of data mining in traffic management: case of city of Isfahan. In: 2010 International conference on electronic computer technology (ICECT), Kuala Lumpur, pp 102–106

    Google Scholar 

  8. Gupta A, Choudhary S, Paul S (2013) DTC: a framework to detect traffic congestion by mining versatile GPS data. In: 2013 1st international conference on emerging trends and applications in computer science (ICETACS), Shillong, pp 97–103

    Google Scholar 

  9. Mulay SA, Dhekne CS, Bapat RM, Budukh TU, Gadgil SD (2015) Intelligent city traffic management and public transportation system. IJCSI Int J Comput Sci 10(3)

    Google Scholar 

  10. Wang C, Li X, Zhou X, Wang A, Nedjah N (2016) Soft computing in big data intelligent transportation systems. Appl Soft Comput 38:1099–1108. ISSN 1568-4946

    Google Scholar 

  11. Tchrakian TT, Basu B et al (2012) Real-time traffic flow forecasting using spectral analysis. IEEE Trans Intell Transp Syst 13(2):519–526

    Article  Google Scholar 

  12. Davis GA, Nihan NL (1991) Nonparametric regression and short term freeway traffic forecasting. J Transp Eng 117(2):178–188

    Article  Google Scholar 

  13. Vythoulkas PC (1993) Alternative approaches to short-term traffic forecasting for use in driver information systems. In: Proceedings of 12th international symposium on traffic flow theory transport, pp 485–506

    Google Scholar 

  14. Lee DH, Zheng W, Shi Q (2006) Short-term freeway traffic flow prediction: Bayesian combined neural network approach. J Transp Eng 132(2):114–121

    Article  Google Scholar 

  15. Jiang X, Adeli H (2005) Dynamic wavelet neural network model for traffic flow fore-casting. J Transp Eng 131(10):771–779

    Article  Google Scholar 

  16. Esteves RM, Pais R, Rong C (2011) K-means Clustering in the cloud—a mahout test. In: 2011 IEEE workshops of international conference on advanced information networking and applications. IEEE, Mar 2011, pp 514–519

    Google Scholar 

  17. Esteves RM, Rong C (2011) Using mahout for clustering wikipedia’s latest articles: a comparison between K-means and fuzzy C-means in the Cloud. In: 2011 IEEE third international conference on cloud computing technology and science. IEEE, Athens, pp 565–569

    Google Scholar 

  18. Dobre C, Xhafa F (2014) Intelligent services for big data science. Future Gener Comput Syst 37:267–281. ISSN 0167-739X

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Mangalore University, Mangalore, 574199, India

    Doreswamy & Osama A. Ghoneim

  2. Computer Science Division, Mathematics Department, Tanta University, Tanta, Egypt

    Osama A. Ghoneim

Authors
  1. Doreswamy
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  2. Osama A. Ghoneim
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Corresponding author

Correspondence to Osama A. Ghoneim .

Editor information

Editors and Affiliations

  1. Department of Studies in Computer Science, University of Mysore, Mysore, Karnataka, India

    D. S. Guru

  2. Department of Master of Computer Application, Maharaja Institute of Technology, Mysore, Karnataka, India

    T. Vasudev

  3. Department of Computer Science and Engineering, Maharaja Institute of Technology, Mysore, Karnataka, India

    H.K. Chethan

  4. Department of Information Science and Engineering, Maharaja Institute of Technology, Mysore, Karnataka, India

    Y.H. Sharath Kumar

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© 2018 Springer Nature Singapore Pte Ltd.

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Doreswamy, Ghoneim, O.A. (2018). Traffic Jams Detection and Congestion Avoidance in Smart City Using Parallel K-Means Clustering Algorithm. In: Guru, D., Vasudev, T., Chethan, H., Kumar, Y. (eds) Proceedings of International Conference on Cognition and Recognition . Lecture Notes in Networks and Systems, vol 14. Springer, Singapore. https://doi.org/10.1007/978-981-10-5146-3_3

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  • DOI: https://doi.org/10.1007/978-981-10-5146-3_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5145-6

  • Online ISBN: 978-981-10-5146-3

  • eBook Packages: EngineeringEngineering (R0)

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