Almudena Sanjurjo-De-No

The growth of micromobility transport in cities has created a new mobility paradigm, but this has also resulted in increased traffic conflicts and collisions. This research focuses on understanding the impacts of micromobility vehicles on pedestrian injury severity in urban areas of Spain between 2016 and 2021. The Random Forest classification model was used to identify the most significant factors and their combinations affecting pedestrian injury severity. To address the issue of unbalanced data, the synthetic minority oversampling technique was employed. The findings indicate that pedestrians’ age, specifically those 70 years or older, is the most important variable in determining injury severity. Additionally, collisions at junctions or on weekends are associated with worse outcomes for pedestrians. The results highlight the combined influence of multiple factors, including offenses and distractions by micromobility users and pedestrians. These factors are more prevalent among younger micromobility users and those riding for leisure or on weekends. To enhance micromobility road safety and reduce pedestrian injuries, separating micromobility traffic from pedestrian areas is recommended, restricting micromobility vehicle use on sidewalks, providing training and information to micromobility users, conducting road safety campaigns, increasing enforcement measures, and incorporating buffer zones in bike lanes near on-street parking.

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.

Extracting driver collision patterns by gender and age regarding offences, collision type and injury severity is very useful in road safety, providing a better understanding on behavior of the different driver groups. Self-Organizing Map (SOM) is the tool proposed for distributing and projecting 145,904 drivers according to 8 offence variables on a 2D map. Thus, drivers who are close in the original 8D space (one dimension per offence variable), will remain so in the projected one (2D). Multivariate driving and collision patterns are explored to support the development of future measures to improve road safety. Tests of proportions are used for shedding light on clusters where driver offence is present. Finally, the SOM results were compared for validation with those of the standard K-Means clustering technique. The results show that the characteristics of road crashes and the severity of injuries depend jointly, i.e., in multivariate (pattern) terms, on gender, age, type of collisions and offences. There are relevant multivariate driver behavior differences in both the type of collisions (and therefore their severity) and the type and number of offences with regard to gender and age of the driver. This research unveils different multivariate driver behavior patterns, providing information about their relative importance (proportion), which helps in road policy decision making in terms of development of prevention measures. The results help in decision making through a potentially better allocation of resources as carried out by road safety regulating offices such as the Spanish Traffic General Directorate (Dirección General de Tráfico, DGT).