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 methodology 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.

Driver Liability Assessment in Vehicle Collisions in Spain.

An accurate estimation of exposure is essential for road collision rate estimation, which is key when evaluating the impact of road safety measures. The quasi-induced exposure method was developed to estimate relative exposure for different driver groups based on its main hypothesis: the not-at-fault drivers involved in two-vehicle collisions are taken as a random sample of driver populations. Liability assignment is thus crucial in this method to identify not-at-fault drivers, but often no liability labels are given in collision records, so unsupervised analysis tools are required. To date, most researchers consider only driver and speed offences in liability assignment, but an open question is if more information could be added. To this end, in this paper, the visual clustering technique of self-organizing maps (SOM) has been applied to better understand the multivariate structure in the data, to find out the most important variables for driver liability, analyzing their influence, and to identify relevant liability patterns. The results show that alcohol/drug use could be influential on liability and further analysis is required for disability and sudden illness. More information has been used, given that a larger proportion of the data was considered. SOM thus appears as a promising tool for liability assessment.