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المؤلفKhanfar, Nour O.
المؤلفAshqar, Huthaifa I.
المؤلفElhenawy, Mohammed
المؤلفHussain, Qinaat
المؤلفHasasneh, Ahmad
المؤلفAlhajyaseen, Wael K.M.
تاريخ الإتاحة2023-04-05T10:48:48Z
تاريخ النشر2022-11-16
اسم المنشورSustainability (Switzerland)
المعرّفhttp://dx.doi.org/10.3390/su142215184
الاقتباسKhanfar, N. O., Ashqar, H. I., Elhenawy, M., Hussain, Q., Hasasneh, A., & Alhajyaseen, W. K. (2022). Application of Unsupervised Machine Learning Classification for the Analysis of Driver Behavior in Work Zones in the State of Qatar. Sustainability, 14(22), 15184.
الرقم المعياري الدولي للكتاب2071-1050
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142753988&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41698
الملخصWork 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.
راعي المشروعThis publication was made possible by the NPRP award (NPRP 9-360-2-150) from the Qatar National Research Fund (a member of The Qatar Foundation).
اللغةen
الناشرElsevier
الموضوعclassification
driving behavior
unsupervised machine learning
vehicle kinematics
work zone
العنوانApplication of Unsupervised Machine Learning Classification for the Analysis of Driver Behavior in Work Zones in the State of Qatar
النوعArticle
رقم العدد22
رقم المجلد14
ESSN2071-1050
dc.accessType Open Access


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