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

Nour O. Khanfar, Huthaifa I. Ashqar, Mohammed Elhenawy, Qinaat Hussain, Ahmad Hasasneh and Wael K. M. Alhajyaseen ()
Additional contact information
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
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

Sustainability, 2022, vol. 14, issue 22, 1-13

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.

Keywords: driving behavior; work zone; vehicle kinematics; classification; unsupervised machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/22/15184/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/22/15184/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:22:p:15184-:d:974236

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().