Understanding e-scooter rider crash severity using a built environment typology: A two-stage clustering and random parameter model analysis

E-scooters are an emerging transport mode that is transforming urban mobility; however, their proliferation has raised concerns about safety. This study combines UK e-scooter crash data with built environment characteristics from the crash locations. A two-stage framework was followed: first, a typology of built environments was developed using K-means++; second, crash severity within each cluster was analysed using a random parameter binary logit model. Four built environment clusters were identified: (1) car-centric and mixed-use zones, (2) commercial and industrial zones, (3) intersection-dense areas, and (4) residential and central areas. Collisions with motor vehicles, younger e-scooter riders, and higher speed limits were the most common risk factors across the clusters, with the first two clusters showing a higher impact of these factors on the likelihood of severe crashes. In the first and second clusters, riding on the carriageway significantly increased injury severity. In the second cluster, three collision types were significant, more than in other clusters where only side-impact collisions were significant. This indicates high e-scooter-motor vehicle friction in the second cluster. Among all collision types, head-on collisions increased the likelihood of severe outcomes more than others. In the third and fourth clusters, peak hours were associated with a lower likelihood of severe crashes, while this variable showed the opposite impact in the first cluster. The results highlight that consideration of the surrounding built environment is paramount when analysing e-scooter crash severity, as unique contributing factors were identified specific to each built environment type, along with varying magnitudes or directions of marginal effects.

Unraveling the relation between cycling accidents and built environment typologies: Capturing spatial heterogeneity through a latent class discrete outcome model

Today, cities seek to transition to more sustainable transportation modes. Cycling is critical in this shift, promoting a more beneficial lifestyle for most. However, cyclists are exposed to many hazardous circumstances or environments, resulting in accidents, injuries, and even death. Transport authorities must understand why accidents occur, to reduce the risk of those who cycle. This study applies a new modeling framework to analyze cycling accident severities. We employ a latent class discrete outcome model, where classes are derived from a Gaussian-Bernoulli mixture, applied to data from Berlin, and augmented with volunteered geographic information. We jointly estimate model components, combining machine learning and econometric approaches, allowing for more intricate and flexible representations while maintaining interpretability. Results show the potential of our approach. Risk factors are indexed depending on where accidents occurred and their contribution. We can discover complex relations between specific built environments and accident characteristics and uncover differences in the impact of certain accident factors on one environment typology but not others. Using multiple data sources also proves helpful as an additional layer of knowledge, providing unique value to understand and model cycling accidents. Another critical aspect of our approach is the potential for simulation, where locations can be examined through simulated accident features to understand the inherent risk of various locations. These findings highlight the ability to capture heterogeneity in accidents and their relation to the built environment. Capturing such relations allows for more direct countermeasures to risky situations or policies to be designed, simulated, and targeted.