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The Maximum Allowable Handlebar Disturbance: An Indicator For The Ex-ante Evaluation Of Cycling Fall Prevention Interventions

Falls due to disturbances are a common cause of serious cycling injuries, yet evaluation approaches to systematically evaluate interventions aimed at improving balance recovery are lacking. Current ex-post evaluations are hindered by sparse crash data, and existing ex-ante approaches often lack generalizability or rely on surrogate measures that are not validated against fall risk. This study introduces the Maximum Allowable Handlebar Disturbance (MAHD), a novel performance indicator that quantifies the largest handlebar disturbance a cyclist can recover from without falling. The MAHD captures the cyclist's resilience to disturbances and provides a direct, interpretable measure of intervention effectiveness. We propose two methods for determining MAHD: (1) controlled treadmill experiments with induced handlebar disturbances and safe fall conditions and (2) simulations using bicycle dynamics and cyclist control models. Together, these methods allow quantitative ex-ante evaluation and systematic comparison of interventions targeting cyclist control, bicycle design, and infrastructure features such as curbs and road shoulders. With further validation, the MAHD offers practical value for researchers, engineers, and policymakers seeking to design safer bicycles, training programs, and road environments and improve evidencebased resource allocation. In the future, this could reduce fall-related cycling injuries.

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The Maximum Allowable Handlebar Disturbance: An Indicator For The Ex-ante Evaluation Of Cycling Fall Prevention Interventions

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