Spatio-temporal analysis and machine learning for traffic accidents prediction

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Al-Dogom, Diena and Aburaed, Nour and Al-Saad, Mina and Almansoori, Saeed; (2020) Spatio-temporal analysis and machine learning for traffic accidents prediction. In: 2019 2nd International Conference on Signal Processing and Information Security, ICSPIS 2019. IEEE, ARE. ISBN 9781728138732 (https://doi.org/10.1109/ICSPIS48135.2019.9045892)

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Abstract

Traffic accidents impose significant problems in our daily life due to the huge social, environmental, and economic expenses associated with them. The rapid development in data science, geographic data collection, and processing methods encourage researchers to evaluate, delineate traffic accident hotspots, and to effectively predict and estimate traffic accidents. In this study, traffic accidents dataset that covers United Kingdom for the time period between 2012-2014 is investigated. The methodology consists of extracting features weights, and then using these weights with statistical methods provided in ArcGIS in order to classify accidents according to severity and perform hotspot analysis and severity prediction. The proposed method can be effectively used by different authorities to implement an improved planning and management approaches for traffic accident reduction. Moreover, it can identify and locate road risk segments where immediate action should be considered.

ORCID iDs

Al-Dogom, Diena, Aburaed, Nour ORCID logoORCID: https://orcid.org/0000-0002-5906-0249, Al-Saad, Mina and Almansoori, Saeed;
  • Item type:Book Section
    ID code:76461
    Dates:
    Date
    Event
    26 March 2020
    Published
    11 November 2019
    Accepted
    Notes:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:17 May 2021 09:39
    Last modified:11 Nov 2024 15:22
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/76461
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