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Modeling of a Vehicle Accident Prediction System Based on a Correlation of Heterogeneous Sources

  • Pablo MarcilloEmail author
  • Lorena Isabel Barona López
  • Ángel Leonardo Valdivieso Caraguay
  • Myriam Hernández-Álvarez
Conference paper
  • 22 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1212)

Abstract

Statistics affirm that traffic accidents are the main cause of death in developing countries. The indicators are alarming, so governments, manufacturers, and researchers have been looking for solutions to mitigate them. Despite all efforts to face this problem, the number of victims remains high. A significant percentage of traffic accidents are caused by external factors, so the search for solutions that use information from multiple sources is crucial. This article presents a traffic accident prediction system based on heterogeneous sources using data mining techniques and machine learning algorithms. The development of this system includes the following tasks: collecting information from different sources, performing cluster analyses and feature selection, generating new datasets, performing machine learning algorithms to define accident rates, and sending traffic rate levels to the vehicles. For this article, we focused on performing cluster analyses to determine high-risk clusters that identify drivers with risky driving patterns.

Keywords

Traffic accident prediction Heterogeneous sources High-risk clusters Machine learning Data mining 

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pablo Marcillo
    • 1
    Email author
  • Lorena Isabel Barona López
    • 1
  • Ángel Leonardo Valdivieso Caraguay
    • 1
  • Myriam Hernández-Álvarez
    • 1
  1. 1.Departamento de Informática y Ciencias de la ComputaciónEscuela Politécnica NacionalQuitoEcuador

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