A model based on cyclist fall experiments which predicts the maximum allowable handlebar disturbance from which a cyclist can recover balance
通过24名不同年龄和技能水平的骑车人实验，提出最大允许车把扰动作为评估防摔倒干预措施的指标，并建立贝叶斯模型预测不同骑车人的该指标，助力设计更安全的自行车和基础设施。
Falls are a significant cause of injury among cyclists, highlighting the need for effective fall prevention interventions. However, ex-ante evaluation of such interventions remains challenging for engineers designing safer infrastructure and bicycles, as well as for safety professionals developing training programs. This study proposes the Maximum Allowable Handlebar Disturbance (MAHD) - the largest external handlebar disturbance a cyclist can recover from - as a performance indicator for evaluating fall prevention interventions. While bicycle dynamics and cyclist control models have the potential to determine this indicator and simulate interventions, their application is currently limited by a lack of validation in predicting the MAHD and the narrow range of interventions that can be incorporated into existing cyclist control models. To address these limitations, we conducted controlled experiments with 24 participants of varying ages and skill levels, exposing them to impulse-like handlebar disturbances that resulted in both recoveries and falls. This dataset, which includes recorded cyclist falls, supports future validation of bicycle dynamics and control models in predicting the MAHD. In addition, using Bayesian Model Averaging, we identified key cyclist factors influencing the MAHD, with forward speed and cyclist balancing skill being critical predictors. Incorporating these predictors into cyclist control models can substantially improve their practical application. These insights were then used to develop a Bayesian multilevel logistic regression model to predict the MAHD for different types of cyclists. Our findings improve the potential for bicycle dynamics and control models to proactively evaluate cyclist fall prevention methods, contributing to safer cycling environments.