England-wide injury-severity analysis of e-scooter riders using a Bayesian spatial field model

As electric scooter (e-scooter) use has expanded, understanding the factors associated with e-scooter rider injury severity has become increasingly important for road safety policy. This study analyses 2,128 crashes involving e-scooters and motor vehicles across England (2020-2023) to identify factors associated with severe and fatal injuries to e-scooter riders. Using the geographic coordinates of crashes, we developed a Bayesian spatial field model implemented via the Stochastic Partial Differential Equation (SPDE) approach for fast Bayesian estimation. Our approach accounts for spatial unobserved heterogeneity (area-level "context" effects) often overlooked in injury severity studies. Results indicate that severe or fatal injuries are more likely among older riders, male riders, and in crashes occurring in darkness, on single carriageways, on roads with speed limits of 40 mph or higher, involving heavy vehicles, at night or early morning, or with e-scooter skidding/overturning, frontal impacts, e-scooters entering main roads, or opponent vehicles moving straight. Conversely, motor vehicles performing moving-off manoeuvres are linked to lower odds of severe injuries. Importantly, the presence of authorised e-scooter trials was not found to be associated with rider injury severity outcomes. Our spatial analysis reveals higher odds of severe injury in parts of north-western and south-eastern England relative to the national average. Our research highlights the importance of vehicle kinematics, road environment, and spatial context in shaping injury severity and support targeted, evidence-based interventions, including infrastructure measures and vehicle-based safety technologies such as blind-spot detection.