Spatio-temporal analysis and machine learning for traffic accidents prediction

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

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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;