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

Impact of Transition Areas on Driving Workload and Driving Behavior in Work Zones: A Naturalistic Driving Study

“…Khanfar et al used driving simulation and unsupervised machine learning clustering to analyze driving behavior in transition areas with different lane closure forms. The numbers of aggressive and conservative drivers in the work area were significantly higher than that of ordinary drivers, and drivers in the left lane were more aggressive than those in the right lane, resulting in vehicle speeds being too high or too low and increasing the dispersion of speed [19]. Wu et al conducted a questionnaire survey on drivers in work zones based on their stated preferences and found that road conditions, traffic conditions, transition area length, and speed limit were important factors affecting drivers' choice of merging location and traffic safety in work zones [20].…”

Real-Time Traffic Density Estimation Using Various Connected Vehicle Penetration Rates: A New Predictive Approach

“…The use of connected vehicle data offers several advantages, including higher data quality compared to other sources like cameras and loop detectors and the ability to collect data from any point within the network, providing a comprehensive understanding of tra c behaviour at any given time. Consequently, transportation agencies are actively working towards facilitating the utilization of probe vehicle data [13], [14].…”