Traffic and Pedestrian Risk Inference Using Harmonic Systems

  • I. Acuña Barrios
  • E. García
  • Daniela López De Luise
  • C. Paredes
  • A. Celayeta
  • M. Sandillú
  • W. Bel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 356)

Abstract

Vehicle and pedestrian risks can be modeled in order to advise drivers and persons. A good model requires the ability to adapt itself to several environmental variations and to preserve essential information about the area under scope. This paper aims to present a proposal based on a Machine Learning extension for timing named Harmonic Systems. A global description of the problem, its relevance, and status of the field is also included.

Keywords

Risk prediction Harmonic systems Time mining 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • I. Acuña Barrios
    • 1
  • E. García
    • 1
  • Daniela López De Luise
    • 1
  • C. Paredes
    • 1
  • A. Celayeta
    • 1
  • M. Sandillú
    • 1
  • W. Bel
    • 1
  1. 1.Researcher at CI2S LabBuenos AiresArgentina

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