Investigating Truck Driver Behavior Under Distracted Conditions to Enhance Road Safety

Abstract: Traffic safety for truck drivers is a crucial aspect of ensuring overall road safety and preventing crashes, especially in complex traffic environments. This study investigates the driving behavior of truck drivers under distracted conditions, such as phone use and rubbernecking, to better understand their impact on road safety. Utilizing a high-fidelity car simulator and eye-tracking technology, the research provides an in-depth analysis of truck drivers' behavior when distracted by phone calls and during rubbernecking incidents. The study simulated a real-world highway environment, specifically a segment of the I-695 highway in Baltimore City and involved forty (40) participants driving through two distinct traffic scenarios including mild and heavy traffic conditions. The aim was to assess truck drivers' reaction times, speed variations, braking frequency, and lateral lane positioning during phone calls. Furthermore, to analyze driver behavior during rubbernecking, a crash scenario was simulated in the opposite direction of the highway to observe changes in drivers' eye movements, vehicle speed, number of braking events, and lateral displacement from neighboring lanes. Advanced machine learning models, including Logistic Regression, Random Forest, and Support Vector Machine (SVM), were employed to predict driver behavior across the different scenarios. The key findings highlight the significant role of driver demographics and experience in shaping responses to distracted driving and rubbernecking. Specifically, male drivers demonstrated notable adjustments in speed and acceleration under distracted conditions, whereas female drivers maintained a more consistent and cautious approach, showing minimal variations in speed, acceleration, and lateral lane positioning. These insights are vital for developing targeted interventions and policies aimed at improving road safety for truck drivers and enhancing overall traffic safety management.