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Road Car Accident Prediction Using a Machine-Learning-Enabled Data Analysis

Author

Listed:
  • Saeid Pourroostaei Ardakani

    (School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
    School of Computer Science, University of Nottingham, Ningbo 315100, China)

  • Xiangning Liang

    (School of Computer Science, University of Nottingham, Ningbo 315100, China)

  • Kal Tenna Mengistu

    (School of Computer Science, University of Nottingham, Ningbo 315100, China)

  • Richard Sugianto So

    (School of Computer Science, University of Nottingham, Ningbo 315100, China)

  • Xuhui Wei

    (School of Computer Science, University of Nottingham, Ningbo 315100, China)

  • Baojie He

    (Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Chongqing 400045, China
    Institute for Smart City of Chongqing University in Liyang, Chongqing University, Liyang 213300, China
    Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, China
    State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510641, China)

  • Ali Cheshmehzangi

    (Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University, Hiroshima 739-8530, Japan
    Department of Architecture and Built Environment, University of Nottingham, Ningbo 315100, China)

Abstract

Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities.

Suggested Citation

  • Saeid Pourroostaei Ardakani & Xiangning Liang & Kal Tenna Mengistu & Richard Sugianto So & Xuhui Wei & Baojie He & Ali Cheshmehzangi, 2023. "Road Car Accident Prediction Using a Machine-Learning-Enabled Data Analysis," Sustainability, MDPI, vol. 15(7), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5939-:d:1110693
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    References listed on IDEAS

    as
    1. Vahid Najafi Moghaddam Gilani & Seyed Mohsen Hosseinian & Meisam Ghasedi & Mohammad Nikookar, 2021. "Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, May.
    2. Jianyu Wang & Huapu Lu & Zhiyuan Sun & Tianshi Wang & Katrina Wang, 2020. "Investigating the Impact of Various Risk Factors on Victims of Traffic Accidents," Sustainability, MDPI, vol. 12(9), pages 1-12, May.
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    Cited by:

    1. Junkai Zhang & Jun Wang & Haoyu Zang & Ning Ma & Martin Skitmore & Ziyi Qu & Greg Skulmoski & Jianli Chen, 2024. "The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends," Sustainability, MDPI, vol. 16(14), pages 1-34, July.
    2. Seyid Abdellahi Ebnou Abdem & Jérôme Chenal & El Bachir Diop & Rida Azmi & Meriem Adraoui & Cédric Stéphane Tekouabou Koumetio, 2023. "Using Logistic Regression to Predict Access to Essential Services: Electricity and Internet in Nouakchott, Mauritania," Sustainability, MDPI, vol. 15(23), pages 1-28, November.

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