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

Antonis Kostopoulos, Thodoris Garefalakis (), Eva Michelaraki, Christos Katrakazas and George Yannis
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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

Sustainability, 2024, vol. 16, issue 14, 1-19

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.

Keywords: road traffic safety; naturalistic driving experiment; driving behavior analysis; driving behavior; harsh events; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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