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Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal

Author

Listed:
  • Paulo Infante

    (CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Mathematics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Gonçalo Jacinto

    (CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Mathematics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Anabela Afonso

    (CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Mathematics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Leonor Rego

    (Department of Mathematics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Pedro Nogueira

    (ICT, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Geosciences, University of Évora, 7000-671 Évora, Portugal)

  • Marcelo Silva

    (ICT, IIFA, University of Évora, 7000-671 Évora, Portugal
    Department of Geosciences, University of Évora, 7000-671 Évora, Portugal)

  • Vitor Nogueira

    (Algoritmi Research Centre, University of Évora, 7000-671 Évora, Portugal
    Department of Informatics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • José Saias

    (Algoritmi Research Centre, University of Évora, 7000-671 Évora, Portugal
    Department of Informatics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Paulo Quaresma

    (Algoritmi Research Centre, University of Évora, 7000-671 Évora, Portugal
    Department of Informatics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Daniel Santos

    (Department of Informatics, ECT, University of Évora, 7000-671 Évora, Portugal)

  • Patrícia Góis

    (Department of Visual Arts and Design, EA, University of Évora, 7000-208 Évora, Portugal)

  • Paulo Rebelo Manuel

    (CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal)

Abstract

Road traffic accidents (RTAs) are a problem with repercussions in several dimensions: social, economic, health, justice, and security. Data science plays an important role in its explanation and prediction. One of the main objectives of RTA data analysis is to identify the main factors associated with a RTA. The present study aims to contribute to the identification of the determinants for the type of RTA: collision, crash, or pedestrian running-over. These factors are essential for identifying specific countermeasures because there is a relevant relationship between the type of RTA and its severity. Daily RTA data from 2016 to 2019 in a district of Portugal were analyzed. A statistical multinomial logit model was fitted. The identified determinants for the type of RTA were geographical (municipality, location, and parking areas), meteorological (air temperature and weather), time of the day (hour, day of the week, and month), driver’s characteristics (gender and age), vehicle’s features (type and age) and road characteristics (road layout and type). The multinomial model results were compared with several machine learning algorithms, since the original data of the type of RTA are severely imbalanced. All models showed poor performance. However, when combining these models with ROSE for class balancing, their performance improved considerably, with the random forest algorithm showing the best performance.

Suggested Citation

  • Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2352-:d:1048641
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    References listed on IDEAS

    as
    1. Maria Rella Riccardi & Francesco Galante & Antonella Scarano & Alfonso Montella, 2022. "Econometric and Machine Learning Methods to Identify Pedestrian Crash Patterns," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    2. Yikai Chen & Kai Wang & Mark King & Jie He & Jianxun Ding & Qin Shi & Changjun Wang & Pingfan Li, 2016. "Differences in Factors Affecting Various Crash Types with High Numbers of Fatalities and Injuries in China," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-12, July.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Yookyung Boo & Youngjin Choi, 2021. "Comparison of Prediction Models for Mortality Related to Injuries from Road Traffic Accidents after Correcting for Undersampling," IJERPH, MDPI, vol. 18(11), pages 1-14, May.
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