Safety analysis of motorcyclists’ overtaking maneuvers

Abstract

This paper aims to investigate the overtaking behavior of motorcyclists in a suburban environment. The goal is to model overtaking duration, identify the factors influencing it, and determine the likelihood of a rider overtaking a vehicle while maintaining critical lateral clearance. Riding data were collected using a passenger car equipped with cameras and a GPS device, which recorded videos of motorcyclists performing maneuvers to overtake it. This setup allowed for capturing natural motorcyclist behavior and avoided the potential limitations of instrumented motorcycle studies, such as bias due to participants being aware of their involvement in the experiment. A total of 119 overtaking maneuvers were recorded. A methodology combining digital image processing algorithms and GPS analysis was employed to characterize the recorded maneuvers. Survival and logistic analyses were then conducted to model the duration of overtaking and lateral clearance, respectively. The hazard-based duration model indicated that the duration of a motorcyclist’s overtaking maneuver is influenced by the final longitudinal distance between the motorcycle and the passed vehicle at the end of the maneuver. Other factors include the speed difference between the motorcycle and the front vehicle at the same instant, and the initial Time-To-Collision (TTC) between the motorcycle and the front vehicle at the beginning of the overtaking. The logistic regression analysis revealed that the probability of overtaking a vehicle with a lateral clearance below the critical threshold increases when the rider does not invade the opposite lane during the overtaking maneuver when a vehicle in the opposite lane induces the motorcyclist to return to the right lane, and as the duration of the overtaking maneuver increases. This research provides valuable contributions to understanding motorcyclist behavior during overtaking maneuvers, aiding in the development of more realistic microsimulation models that account for actual rider behavior. Additionally, the study contributes to the development of Advanced Rider Assistance Systems aimed at guiding motorcyclists to make safer overtaking decisions and reduce significant risk exposure from complex overtaking maneuvers.