Analysis and prediction of injury severity in single micromobility crashes with Random Forest

Urban micromobility represents a significant shift towards sustainable cities, underscoring the paramount importance of its safety. With the surge in micromobility adoption, collisions involving micromobility devices, such as bicycles and e-scooters, have surged in recent years. The second most common crash type involving these vehicles is one that only involves a micromobility vehicle (single micromobility crashes). This study analyzed 6030 single micromobility crashes that occurred in Spanish urban areas from 2016 to 2020. The Random Forest method-ology was applied to create a classification model for the purpose of characterizing these crashes, predicting their injury severity, and identifying the primary influencing factors. To address the issue of imbalanced data, resulting from the relatively smaller dataset of fatal and seriously injured crashes compared to slightly injured ones, the Synthetic Minority Oversampling Technique (SMOTE) was applied. The results indicate that certain behaviors, such as not wearing a helmet, riding for leisure, and instances of speeding violations, have the potential to increase injury severity. Additionally, crashes occurring at intersections or at cycle lanes with bad pavement conditions are likely to result in more severe outcomes. Furthermore, the concurrent presence of various other factors also contributes to an escalation in crash injury severity. These findings have the potential to provide valuable insights to authorities, assisting them in the decision-making process to enhance micromobility safety and thereby promoting the creation of more equitable and sustainable urban environments.