A mechanistic approach to modeling omnidirectional motorcyclist injury risk.

OBJECTIVE

As of 2022, motorcyclist fatalities in the United States had risen 38% since 2010, representing 15% of all U.S. traffic fatalities. Recently developed injury risk models have sought to better predict injury potential for certain collision configurations involving motorcycles using relative speed as a primary predictor variable. Advancing the state-of-the-art, this study developed injury risk models for motorcyclist collisions with passenger vehicles across all planar configurations and incorporated biomechanically-relevant predictor variables including a novel speed parameter.

METHODS

We analyzed real-world crash data from the German In-Depth Accident Study (GIDAS) (1999-2023) to examine motorcyclist injury patterns and create injury risk functions at the MAIS2 + F, 3 + F, 4 + F, and 5 + F levels. Biomechanically relevant variables, including age (via a spline function), sex, and a geometric-based assessment of motorcyclist post-impact response (i.e., potential for a normal projection), were considered. Effective Collision Speed, combining passenger vehicle and motorcycle speeds while accounting for reduced engagement associated with frictional effects in side impacts, was employed as an important predictor. We analyzed the impact of reweighting the dataset to German national statistics, addressing GIDAS' bias toward severe and fatal collisions.

RESULTS

The dataset comprised 2,499 passenger-vehicle to motorcycle collisions, of which 59% involved contact with the front of the passenger vehicle, 25% the side, and 16% the rear. 37% of motorcyclists sustained AIS2 + F injuries and 11% sustained AIS3 + F injuries. At the MAIS3 + F level, the lower extremities were the most commonly injured body region, followed by the thorax and head. Age significantly influenced injury risk at MAIS2 + F and MAIS3 + F levels. A potential normal projection was associated with higher injury risk, significant only for MAIS2 + F. Effective Collision Speed emerged as the sole significant predictor for higher severity levels.

CONCLUSIONS

These findings highlight the importance of incorporating biomechanical factors and refined speed metrics into motorcyclist injury risk models. The proposed Effective Collision Speed demonstrated strong predictive capability, offering a more comprehensive approach for assessing injury potential across varied crash configurations.