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AuthorKhanfar, Nour O.
AuthorAshqar, Huthaifa I.
AuthorElhenawy, Mohammed
AuthorHussain, Qinaat
AuthorHasasneh, Ahmad
AuthorAlhajyaseen, Wael K.M.
Available date2023-04-05T10:48:48Z
Publication Date2022-11-16
Publication NameSustainability (Switzerland)
Identifierhttp://dx.doi.org/10.3390/su142215184
CitationKhanfar, 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.
ISSN2071-1050
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142753988&origin=inward
URIhttp://hdl.handle.net/10576/41698
AbstractWork 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.
SponsorThis 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).
Languageen
PublisherElsevier
Subjectclassification
driving behavior
unsupervised machine learning
vehicle kinematics
work zone
TitleApplication of Unsupervised Machine Learning Classification for the Analysis of Driver Behavior in Work Zones in the State of Qatar
TypeArticle
Issue Number22
Volume Number14
ESSN2071-1050
dc.accessType Open Access


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