Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing

This study aims to investigate contributing factors to potential collision risks during lane-changing processes from the perspective of vehicle groups and explore the unobserved heterogeneity of individual lane-changing maneuvers. Vehicular trajectory data, extracted from the Federal Highway Administration's Next Generation Simulation dataset, are utilized and 579 lane-changing vehicle groups are examined. Stopping distance indexes are developed to evaluate the potential collision risks of lane-changing vehicle groups. Three mixed binary logit models and three mixed logit models with heterogeneity in means and variances are established based on different perception reaction time. Model estimation results show that several variables significantly affect the risk status of lane-changing vehicle groups, including the mean values of clearance distance and speed differences between the leading vehicle in the current lane and the subject vehicle, standard deviations of clearance distance, and speed differences between these two vehicles, as well as standard deviations of the speed difference between the subject vehicle and the following vehicle in the target lane. Interestingly, the influences of the last three variables differ considerably across the observations and the mean of the random parameter for standard deviations of clearance distance between CLV and SV is associated with the mean speed difference between CLV and SV. Since one of the explanations is individual heterogeneity, personalized designs for advanced driver assistance system would be an effective measure to reduce the risk.

Automated summary

This study investigates potential collision risks during lane-changing processes and finds that variables such as clearance distance, speed differences, and perception reaction time significantly affect the risk status of lane-changing vehicle groups. The study also identifies the impact of individual heterogeneity on the results and suggests personalized designs for advanced driver assistance systems to reduce risks.