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Short-Term Traffic Congestion Forecasting Using Hybrid Metaheuristics and Rule-Based Methods: A Comparative Study

  • Pedro Lopez-Garcia
  • Eneko Osaba
  • Enrique Onieva
  • Antonio D. Masegosa
  • Asier Perallos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9868)

Abstract

In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have been chosen. The used time horizons are 5, 15 and 30 min. Results show that the hybrid technique improves those results obtained by the techniques of the state of the art. In this way, the performed experimentation shows the competitiveness of the proposal in this area of application.

Keywords

Genetic algorithms Cross entropy Classification Machine learning Hybrid optimization Fuzzy rule-based systems Intelligent transportation systems 

Notes

Acknowledgments

Authors work was supported by TIMON Project. This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 636220.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Pedro Lopez-Garcia
    • 1
    • 2
  • Eneko Osaba
    • 1
    • 2
  • Enrique Onieva
    • 1
    • 2
  • Antonio D. Masegosa
    • 1
    • 2
    • 3
  • Asier Perallos
    • 1
    • 2
  1. 1.DeustoTech-Fundacion Deusto, Deusto FoundationBilbaoSpain
  2. 2.Faculty of EngineeringUniversity of DeustoBilbaoSpain
  3. 3.IKERBASQUE, Basque Foundation for ScienceBilbaoSpain

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