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Forecasting Traffic Flow in Big Cities Using Modified Tucker Decomposition

  • Manish BhanuEmail author
  • Shalini Priya
  • Sourav Kumar Dandapat
  • Joydeep Chandra
  • João Mendes-Moreira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

An efficient traffic-network is an essential demand for any smart city. Usually, city traffic forms a huge network with millions of locations and trips. Traffic flow prediction using such large data is a classical problem in intelligent transportation system (ITS). Many existing models such as ARIMA, SVR, ANN etc, are deployed to retrieve important characteristics of traffic-network and for forecasting mobility. However, these methods suffer from the inability to handle higher data dimensionality. The tensor-based approach has recently gained success over the existing methods due to its ability to decompose high dimension data into factor components. We present a modified Tucker decomposition method which predicts traffic mobility by approximating very large networks so as to handle the dimensionality problem. Our experiments on two big-city traffic-networks show that our method reduces the forecasting error, for up to 7 days, by around 80% as compared to the existing state of the art methods. Further, our method also efficiently handles the data dimensionality problem as compared to the existing methods.

Keywords

ODM CP decomposition Tucker Time-series CUR Traffic flow 

Notes

Acknowledgment

This work is funded by Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020”, a project financed by North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Manish Bhanu
    • 1
    Email author
  • Shalini Priya
    • 1
  • Sourav Kumar Dandapat
    • 1
  • Joydeep Chandra
    • 1
  • João Mendes-Moreira
    • 2
  1. 1.Indian Institute of TechnologyPatnaIndia
  2. 2.LIAAD - INESC TEC, Faculty of EngineeringUniversity of PortoPortoPortugal

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