Abstract
This study examines how novice driving behaviors affect highway flow and collision potential. Driving behaviors of candidates receiving driving training are analyzed for the first time using drone images, in-car footage, and image processing methods. Driving parameters such as standstill distance [CC0], acceleration/deceleration, perception-reaction times, and speeds are extracted using image processing and field observation. These novice driver (ND) parameters are then incorporated into the VISSIM traffic micro-simulation model as a separate driving behavior dataset. The impact of NDs on traffic under different compositions and the resulting crash potential is then assessed. Safety analysis using the Collision Potential Index (CPI) reveal a 35% increase in CPI with only 10% novice drivers, while mobility analysis indicates a 14% average speed decrease with 50% ND traffic composition. Interestingly, a decrease in CPI values is observed when the ND ratio increases to 40% and 50%, which is explained by the more cautious behavior of experienced drivers and a decrease in traffic flow speed. The use of real-world data increases the authenticity and reliability of the study. The findings contribute to the understanding of the risks associated with novice drivers, highlighting the need for effective safety measures. This study provides valuable insights for policy makers and traffic safety experts to reduce the threats posed by inexperienced drivers and regulate the behavior of experienced drivers.