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Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents

Author

Listed:
  • Sarah Najm Abdulwahid

    (College of Graduate Studies, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Moamin A. Mahmoud

    (Institute of Informatics and Computing in Energy, Department of Computing, College of Computing and Informatics, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Nazrita Ibrahim

    (Institute of Informatics and Computing in Energy, Department of Computing, College of Computing and Informatics, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Bilal Bahaa Zaidan

    (Future Technology Research Center, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

  • Hussein Ali Ameen

    (Department of Computer Techniques Engineering, Al-Mustaqbal University College, Hillah 51001, Iraq)

Abstract

Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents.

Suggested Citation

  • Sarah Najm Abdulwahid & Moamin A. Mahmoud & Nazrita Ibrahim & Bilal Bahaa Zaidan & Hussein Ali Ameen, 2022. "Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents," IJERPH, MDPI, vol. 19(13), pages 1-20, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:7704-:d:846002
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    References listed on IDEAS

    as
    1. Sarah Najm Abdulwahid & Moamin A. Mahmoud & Bilal Bahaa Zaidan & Abdullah Hussein Alamoodi & Salem Garfan & Mohammed Talal & Aws Alaa Zaidan, 2022. "A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations," IJERPH, MDPI, vol. 19(6), pages 1-38, March.
    2. Laura Eboli & Carmen Forciniti, 2020. "The Severity of Traffic Crashes in Italy: An Explorative Analysis among Different Driving Circumstances," Sustainability, MDPI, vol. 12(3), pages 1-19, January.
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    Cited by:

    1. Yuning Wang & Shuocheng Yang & Jinhao Li & Shaobing Xu & Jianqiang Wang, 2023. "An Emergency Driving Intervention System Designed for Driver Disability Scenarios Based on Emergency Risk Field," IJERPH, MDPI, vol. 20(3), pages 1-20, January.

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