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Modeling and Sustainability Implications of Harsh Driving Events: A Predictive Machine Learning Approach

Author

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  • Antonis Kostopoulos

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 IroonPolytechniou Str., 157 73 Athens, Greece)

  • Thodoris Garefalakis

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 IroonPolytechniou Str., 157 73 Athens, Greece)

  • Eva Michelaraki

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 IroonPolytechniou Str., 157 73 Athens, Greece)

  • Christos Katrakazas

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 IroonPolytechniou Str., 157 73 Athens, Greece)

  • George Yannis

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 IroonPolytechniou Str., 157 73 Athens, Greece)

Abstract

Human behavior significantly contributes to severe road injuries, underscoring a critical road safety challenge. This study addresses the complex task of predicting dangerous driving behaviors through a comprehensive analysis of over 356,000 trips, enhancing existing knowledge in the field and promoting sustainability and road safety. The research uses advanced machine learning algorithms (e.g., Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and K-Nearest Neighbors) to categorize driving behaviors into ‘Dangerous’ and ‘Non-Dangerous’. Feature selection techniques are applied to enhance the understanding of influential driving behaviors, while k-means clustering establishes reliable safety thresholds. Findings indicate that Gradient Boosting and Multilayer Perceptron excel, achieving recall rates of approximately 67% to 68% for both harsh acceleration and braking events. This study identifies critical thresholds for harsh events: (a) 48.82 harsh accelerations and (b) 45.40 harsh brakings per 100 km, providing new benchmarks for assessing driving risks. The application of machine learning algorithms, feature selection, and k-means clustering offers a promising approach for improving road safety and reducing socio-economic costs through sustainable practices. By adopting these techniques and the identified thresholds for harsh events, authorities and organizations can develop effective strategies to detect and mitigate dangerous driving behaviors.

Suggested Citation

  • Antonis Kostopoulos & Thodoris Garefalakis & Eva Michelaraki & Christos Katrakazas & George Yannis, 2024. "Modeling and Sustainability Implications of Harsh Driving Events: A Predictive Machine Learning Approach," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6151-:d:1438011
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    References listed on IDEAS

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    1. Neska Haouij & Jean-Michel Poggi & Raja Ghozi & Sylvie Sevestre-Ghalila & Mériem Jaïdane, 2019. "Random forest-based approach for physiological functional variable selection for driver’s stress level classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 157-185, March.
    2. Ke Wang & Qingwen Xue & Jian John Lu, 2021. "Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework," IJERPH, MDPI, vol. 18(14), pages 1-18, July.
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