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Spatial-Temporal K Nearest Neighbors Model on MapReduce for Traffic Flow Prediction

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Intelligent Data Engineering and Automated Learning – IDEAL 2018 (IDEAL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11314))

Abstract

Researches in the area of short-term traffic flow forecasting are important for traffic flow management in intelligent transport systems. In this paper, a distributed model for short-term traffic flow prediction based on the k nearest neighbors method is presented. This model takes into account spatial and temporal traffic flow distribution. We define a feature vector for a targeted road segment using traffic flow on segments in a compact area at different time intervals. To reduce the dimensionality of the feature vector, we use principal component analysis procedure. The proposed model is based on MapReduce technology and implemented using an Apache Spark framework. An experimental study data is obtained from the transportation network of Samara, Russia.

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Acknowledgments

This work was supported by Project no. RFMEFI57518X0177 by the Ministry of Education and Science of the Russian Federation.

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Authors and Affiliations

  1. Samara National Research University, 34, Moskovskoye shosse, Samara, 443086, Russia

    Anton Agafonov & Alexander Yumaganov

Authors
  1. Anton Agafonov
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  2. Alexander Yumaganov
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Corresponding author

Correspondence to Anton Agafonov .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Autonomous University of Madrid, Madrid, Spain

    David Camacho

  3. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

  4. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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Agafonov, A., Yumaganov, A. (2018). Spatial-Temporal K Nearest Neighbors Model on MapReduce for Traffic Flow Prediction. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_27

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  • DOI: https://doi.org/10.1007/978-3-030-03493-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03492-4

  • Online ISBN: 978-3-030-03493-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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