Show simple item record

AuthorKhanfar, Nour O.
AuthorElhenawy, Mohammed
AuthorAshqar, Huthaifa I.
AuthorHussain, Qinaat
AuthorAlhajyaseen, Wael K.M.
Available date2022-10-19T07:21:23Z
Publication Date2022-07
Publication NameInternational Journal of Injury Control and Safety Promotion
CitationKhanfar, N. O., Elhenawy, M., Ashqar, H. I., Hussain, Q., & Alhajyaseen, W. K. (2022). Driving behavior classification at signalized intersections using vehicle kinematics: application of unsupervised machine learning. International journal of injury control and safety promotion, 1-11.
AbstractDriving behavior is considered as a unique driving habit of each driver and has a significant impact on road safety. This study proposed a novel data-driven Machine Learning framework that can classify driving behavior at signalized intersections considering two different signal conditions. To the best of our knowledge, this is the first study that investigates driving behavior at signalized intersections with two different conditions that are mostly used in practice, i.e., the control setting with the signal order of green-yellow-red and a flashing green setting with the signal order of green-flashing green-yellow-red. A driving simulator dataset collected from participants at Qatar University’s Qatar Transportation and Traffic Safety Center, driving through multiple signalized intersections, was used. The proposed framework extracts volatility measures from vehicle kinematic parameters including longitudinal speed and acceleration. K-means clustering algorithm with elbow method was used as an unsupervised machine learning to cluster driving behavior into three classes (i.e., conservative, normal, and aggressive) and investigate the impact of signal conditions. The framework confirmed that in general driving behavior at a signalized intersection reflects drivers’ habits and personality rather than the signal condition, still, it manifests the intersection nature that usually requires drivers to be more vigilant and cautious. Nonetheless, the results suggested that flashing green condition could make drivers more conservative, which could be due to the limited capabilities of human to estimate the remaining distance and the prolonged duration of the additional flashing green interval. The proposed framework and findings of the study were promising that can be used for clustering drivers into different styles for different conditions and might be beneficial for policymakers, researchers, and engineers.
SponsorThe NPRP award [NPRP 9- 360-2-150] from the Qatar National Research Fund (a member of Qatar Foundation).
PublisherTaylor & Francis
SubjectDriving behavior
driving style
signalized intersection
vehicle kinematics
volatility measures
TitleDriving behavior classification at signalized intersections using vehicle kinematics: Application of unsupervised machine learning

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record