Bridging the gap: Mechanistic-based cyclist injury risk curves using two decades of crash data

Abstract

Objective

Injury risk curves are vital in quantifying the relative safety consequences of real-world collisions. Previous injury risk curves for bicycle-passenger vehicle crashes have predominantly focused on frontal impacts. This creates a gap in cyclist injury risk assessment for other geometric crash configurations. The goal of this study was to create an “omnidirectional” injury risk model, informed by known injury causing mechanisms, that is applicable to most geometric configurations.

Methods

We used data from years 1999–2022 of the German In-Depth Accident Study (GIDAS). We describe the pattern of injuries for cyclists involved in collisions with passenger vehicles, and we developed injury risk functions at various AIS levels for these collisions. A mechanistic-based approach accounting for biomechanically-relevant variables was used to select model parameters a priori. Cyclist age (including children) and sex were regarded as relevant predictors of injury risk. Speed and impact geometry were captured through a novel predictor, Effective Collision Speed, which transforms the vehicle and cyclist speeds into a single value and incorporates frictional considerations observed during side engagements. Cyclist engagement with the vehicle was captured with a variable demonstrating the potential for a normal projection. We additionally present analyses weighted toward German nationwide data.

Results

We identified 6,576 cyclists involved in collisions with passenger vehicles. AIS3+ cyclist injuries occurred most often in the head, thorax, and lower extremities. Effective Collision Speed was a strong predictor of injury risk. Collisions with a potential for a normal projection were associated with increased risk, though this was only significant at the MAIS2+F severity level. Younger children had slightly higher injury risk compared to young adults, while elderly cyclists had the highest risk of AIS3+ injury. Sex was a significant predictor only for the MAIS2+F injury risk curves.

Significance

U.S. cyclist fatalities increased 55% from 2010 to 2021. To reduce injuries and fatalities, it is crucial to understand cyclist injury risk. This study builds on previous analyses by including children, incorporating additional mechanistic predictors, broadening the scope of included crashes, and using weighting to generalize these estimates toward national German statistics.