England-wide injury-severity analysis of e-scooter riders using a Bayesian spatial field model
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.
E-scooter safety, Laplace approximation, Rider injury severity, Spatial dependence, Spatially varying covariates
Zhao, Jingjing
3b6c74ce-2cee-4cf5-bb74-a5385523120f
Konstantinoudis, Garyfallos
5917e284-1096-4e05-b7a0-ad807916864c
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
1 July 2026
Zhao, Jingjing
3b6c74ce-2cee-4cf5-bb74-a5385523120f
Konstantinoudis, Garyfallos
5917e284-1096-4e05-b7a0-ad807916864c
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Zhao, Jingjing, Konstantinoudis, Garyfallos and Heydari, Shahram
(2026)
England-wide injury-severity analysis of e-scooter riders using a Bayesian spatial field model.
Accident Analysis & Prevention, 232, [108517].
(doi:10.1016/j.aap.2026.108517).
Abstract
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.
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escooter injury severity spatial_aap_R1 final PURE
More information
Accepted/In Press date: 18 March 2026
e-pub ahead of print date: 28 March 2026
Published date: 1 July 2026
Additional Information:
Publisher Copyright:
© 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/
Keywords:
E-scooter safety, Laplace approximation, Rider injury severity, Spatial dependence, Spatially varying covariates
Identifiers
Local EPrints ID: 511813
URI: http://eprints.soton.ac.uk/id/eprint/511813
ISSN: 0001-4575
PURE UUID: aef7040d-40d3-423f-b6a1-6638cca896a3
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Date deposited: 03 Jun 2026 16:47
Last modified: 06 Jun 2026 01:58
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Author:
Jingjing Zhao
Author:
Garyfallos Konstantinoudis
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