Empirical analysis of car-following behavior: Impacts of driver demographics, leading vehicle types, and speed limits on driver behavior and safety

• Car-following behavior was empirically studied in a driving simulator environment. • Potential safety concerns were observed at a speed limit of 50 km/h compared to a speed limit of 80 km/h. • A higher average deceleration was observed when following a motorbike compared to other vehicle types. • Demographic characteristics significantly impact the car-following behavior. • Females were identified as having a higher risk of crashes compared to males. Car-following behavior is the most fundamental and common driving behavior and is crucial for road safety and traffic efficiency. Traffic flow dynamics are greatly affected by this behavior, and driver-related factors in car-following behavior have been identified as a key cause of rear-end crashes. Despite extensive research on car-following behavior, a gap remains in understanding how this behavior manifests itself in culturally diverse driver populations and heterogeneous driving conditions. The aim of this study was to empirically investigate the effects of individual characteristics, leading vehicle types, posted speed limits and deceleration rates of the leading vehicle on car-following behavior. To this end, the car-following behavior of 61 participants was investigated in eight different scenarios involving a motorbike, sedan, SUV, and truck as the leading vehicle under two different speed limits, i.e., 50 km/h and 80 km/h in a driving simulator environment. The results showed that considerable variations in car-following behaviors exist depending on gender, age, driving experience, educational levels, and the type of leading vehicle, as well as speed limits and deceleration rates. The risk of rear-end crash was found to be higher at the lower speed limit and with a two-wheeler (motorbike) as the leading vehicle. Additionally, females were identified as a having higher crash risk than males. In summary, this study provides valuable insights that could be applied to enhance road safety, such as tailoring targeted training materials for high-risk groups and informing policy decisions. Incorporating these insights into model calibration can lead to more accurate and realistic simulations that capture the complexities of real-world driving scenarios.