England-wide injury-severity analysis of e-scooter riders using a Bayesian spatial field model
Year: 2026
Type: article
Study design: Observational Study
Abstract: The rising popularity of electric scooters (e-scooters) has led to increased crashes, posing significant road safety challenges. This study analyses 2,128 crashes involving e-scooters and motor vehicles across England (2020-2023) to identify factors associated with severe 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 approach for fast Bayesian estimation. Our approach accounts for spatial unobserved heterogeneity often overlooked in injury severity studies, improving model fit substantially. We demonstrate the ease with which spatially varying coefficients can be incorporated into the analysis, even in a computationally intensive setting. 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. Our spatial analysis revealed that in parts of northwestern and southeastern England, e-scooter riders exhibit higher odds of severe injury compared with the national average. These findings inform evidence-based policies to enhance e-scooter safety in England and elsewhere.
Source: Accident Analysis & Prevention
Institutions: University of Southampton, Imperial College London
Language: English
Topic: Traffic and Road Safety
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
OpenAlex ID: W7142005651