Uncovering the role of restraint usage in driver ejection: a data mining investigation of fatal and injury crashes.

Objective: This study aims to identify key contributing factors to driver ejection in vehicle crashes. By analyzing crash characteristics, restraint usage, and environmental conditions, the study provides data-driven insights to mitigate ejection risks and improve roadway safety interventions.

Method: This study, utilizing comprehensive Texas crash data from 2017 to 2022; investigates this critical concern by applying an ARM algorithm to uncover key patterns and interactions among various factors contributing to ejection events. By analyzing the specific circumstances under which driver ejections occur, both with and without restraint usage, the study examines the dynamics of these crashes and their contributing factors.

Results: The analysis reveals that most fatal and severe ejection crashes involving restrained drivers are strongly associated with vehicle overturns, compounded by the interaction of dark lighting conditions, curved road segments, high posted speed limits (70 mph or higher), and two-lane, two-way rural roads. In contrast, ejection crashes involving unrestrained drivers are predominantly linked to dark lighting conditions, rural two-lane roads, and pickup or SUV-type vehicles, where the absence of restraint significantly increases the risk of fatal or severe injuries. Airbag deployment status, particularly cases of non-deployment or multiple deployments, has been observed in combination with other crash factors, such as high-speed impacts and overturning, contributing to an increased likelihood of ejection.

Conclusions: These findings suggest targeted safety solutions including better roadway lighting and guardrails to reduce overturning-induced ejections, stricter rural seatbelt enforcement, and enhanced vehicle restraint systems to limit unintentional occupant movement.