Traffic injury prevention · 2025
Injury risk curves for motorcycles in the United States.
СтатьяНазвание и аннотация: Перевод в очереди
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Terranova Paolo, Guo Feng, Doerzaph Zachary, Perez Miguel A
Аннотация
OBJECTIVE: This study introduces a comprehensive injury risk model for motorcycle collisions tailored to the United States (U.S.). The proposed innovative approach enables the prediction of injury risk across the full range of crash speed and vehicle orientations, making it an essential tool for evaluating emerging safety measures for motorcycles.
METHODS: Data from the Motorcycle Crash Causation Study (MCCS) is used to train two multivariate regression models for predicting motorcycle injury risk. The data is weighted to align with national crash statistics and effectively represents the U.S. crash population. The models adopt motorcycle impact speed and vehicle-relative speed as key predictors while incorporating rider age, helmet use, and opponent vehicle type as covariates. The motorcycle's principal direction of force (PDoF) is also employed as a surrogate metric for the vehicle orientation, enabling the models to account for the full range of two-vehicle crash configurations.
RESULTS: The analysis clearly indicates that the motorcycle front-end crash-i.e., the motorcycle's front collides with any side of the opposing vehicle-is the most dangerous crash configuration. Additionally, the results demonstrate significant variability in injury likelihood based on the PDoF, emphasizing the influence of vehicle alignment and orientation on riders' injury outcomes.
CONCLUSIONS: This study provides the first comprehensive injury risk models for motorcycle-involved two-vehicle collisions in the U.S., offering critical insights into the role of speed and vehicle orientation in injury outcomes. While further validation with larger datasets is required, the findings serve as a foundation for the evaluation of the safety benefits of emerging traffic safety systems.
METHODS: Data from the Motorcycle Crash Causation Study (MCCS) is used to train two multivariate regression models for predicting motorcycle injury risk. The data is weighted to align with national crash statistics and effectively represents the U.S. crash population. The models adopt motorcycle impact speed and vehicle-relative speed as key predictors while incorporating rider age, helmet use, and opponent vehicle type as covariates. The motorcycle's principal direction of force (PDoF) is also employed as a surrogate metric for the vehicle orientation, enabling the models to account for the full range of two-vehicle crash configurations.
RESULTS: The analysis clearly indicates that the motorcycle front-end crash-i.e., the motorcycle's front collides with any side of the opposing vehicle-is the most dangerous crash configuration. Additionally, the results demonstrate significant variability in injury likelihood based on the PDoF, emphasizing the influence of vehicle alignment and orientation on riders' injury outcomes.
CONCLUSIONS: This study provides the first comprehensive injury risk models for motorcycle-involved two-vehicle collisions in the U.S., offering critical insights into the role of speed and vehicle orientation in injury outcomes. While further validation with larger datasets is required, the findings serve as a foundation for the evaluation of the safety benefits of emerging traffic safety systems.