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Application of Unsupervised Machine Learning Classification for the Analysis of Driver Behavior in Work Zones in the State of Qatar

Author

Listed:
  • Nour O. Khanfar

    (Natural, Engineering and Technology Sciences Department, Arab American University, 13 Zababde, Jenin P.O. Box 240, Palestine)

  • Huthaifa I. Ashqar

    (Natural, Engineering and Technology Sciences Department, Arab American University, 13 Zababde, Jenin P.O. Box 240, Palestine
    Precision Systems, Inc., Washington, DC 20003, USA)

  • Mohammed Elhenawy

    (CARRS-Q—Centre for Accident Research and Road Safety, Queensland University of Technology, Brisbane, QLD 4059, Australia)

  • Qinaat Hussain

    (Qatar Transportation and Traffic Safety Centre, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar)

  • Ahmad Hasasneh

    (Natural, Engineering and Technology Sciences Department, Arab American University, 13 Zababde, Jenin P.O. Box 240, Palestine)

  • Wael K. M. Alhajyaseen

    (Qatar Transportation and Traffic Safety Centre, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar
    Department of Civil & Architectural Engineering, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar)

Abstract

Work zone areas are commonly known as crash-prone areas. Thus, they usually receive high priority by road operators as drivers and workers have higher chances of being involved in road crashes. The paper aims to investigate driving behavior in work zones using unsupervised machine learning and vehicle kinematic data. A dataset of 67 participants was gathered through an experiment using a driving simulator located at the Qatar Transportation and Traffic Safety Center (QTTSC). The study considered two different work zone scenarios where the leftmost lane was closed for maintenance. In the first scenario, drivers drove on the leftmost lane (Drive 1), while in the second, they drove on the second leftmost lane (Drive 2). The results show that the number of aggressive and conservative drivers was surprisingly more than normal drivers, as most participants either cautiously drove through or failed to drive without being aggressive. The results also show that drivers acted more aggressively in the leftmost lane rather than in the second leftmost lane. We also found that female drivers and drivers with relatively little driving experience were more likely to be aggressive as they drove through a work zone. The framework was found to be promising and can help policymakers take optimal safety countermeasures in work zones during construction.

Suggested Citation

  • Nour O. Khanfar & Huthaifa I. Ashqar & Mohammed Elhenawy & Qinaat Hussain & Ahmad Hasasneh & Wael K. M. Alhajyaseen, 2022. "Application of Unsupervised Machine Learning Classification for the Analysis of Driver Behavior in Work Zones in the State of Qatar," Sustainability, MDPI, vol. 14(22), pages 1-13, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15184-:d:974236
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    References listed on IDEAS

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    1. Fadi Shahin & Wafa Elias & Yehiel Rosenfeld & Tomer Toledo, 2022. "An Optimization Model for Highway Work Zones Considering Safety, Mobility, and Project Cost," Sustainability, MDPI, vol. 14(3), pages 1-18, January.
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