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Machine Learning Insights on Driving Behaviour Dynamics among Germany, Belgium, and UK Drivers

Author

Listed:
  • Stella Roussou

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Iroon Polytechniou Str., 15773 Athens, Greece)

  • Thodoris Garefalakis

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Iroon Polytechniou Str., 15773 Athens, Greece)

  • Eva Michelaraki

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Iroon Polytechniou Str., 15773 Athens, Greece)

  • Tom Brijs

    (Transportation Research Institute (IMOB), School of Transportation Sciences, UHasselt–Hasselt University, 3500 Hasselt, Belgium)

  • George Yannis

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Iroon Polytechniou Str., 15773 Athens, Greece)

Abstract

The i-DREAMS project has a core objective: to establish a comprehensive framework that defines, develops, and validates a context-aware ‘Safety Tolerance Zone’ (STZ). This zone is crucial for maintaining drivers within safe operational boundaries. The primary focus of this research is to conduct a detailed comparison between two machine learning approaches: long short-term memory networks and shallow neural networks. The goal is to evaluate the safety levels of participants as they engage in natural driving experiences within the i-DREAMS on-road field trials. To accomplish this objective, the study gathered a series of trips from a sample group consisting of 30 German drivers, 43 Belgian drivers, and 26 drivers from the United Kingdom. These trips were then input into the aforementioned machine learning methods to reveal the factors contributing to unsafe driving behaviour across various experiment stages. The results obtained highlight the significant positive impact of i-DREAMS’ real-time interventions and post-trip assessments on enhancing driving behaviour. Furthermore, it is worth noting that neural networks demonstrated superior performance compared to other algorithms considered within this research context.

Suggested Citation

  • Stella Roussou & Thodoris Garefalakis & Eva Michelaraki & Tom Brijs & George Yannis, 2024. "Machine Learning Insights on Driving Behaviour Dynamics among Germany, Belgium, and UK Drivers," Sustainability, MDPI, vol. 16(2), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:518-:d:1314551
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    References listed on IDEAS

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    1. Khaled Assi, 2020. "Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models," IJERPH, MDPI, vol. 17(20), pages 1-16, October.
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