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Exploring e-scooter risk factors based on interpretable machine learning framework.

Amjad Pervez1, Arshad Jamal2

  • 1School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.

Journal of Safety Research
|September 10, 2025
PubMed
Summary
This summary is machine-generated.

Electric scooter (e-scooter) rider safety is a growing concern. This study identifies key risk factors for severe e-scooter crashes, including rider gender and road conditions, to inform safety recommendations.

Keywords:
E-scooter safetyElectric scootersInjury severityMachine learning modelsMicro-mobilityRisk factorsUrban safety

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Area of Science:

  • Urban mobility studies
  • Transportation safety research
  • Data science in public health

Background:

  • Rising e-scooter use presents urban mobility benefits but increases injury risks.
  • Existing research lacks specific risk factors and interpretable analysis for e-scooter crashes.

Purpose of the Study:

  • Identify specific risk factors for e-scooter crashes using advanced analytics.
  • Predict injury severity in e-scooter incidents.

Main Methods:

  • Analysis of 2,400 UK e-scooter crash records (STATS19 database).
  • Application of machine learning models (LightGBM) for injury severity prediction.
  • Utilized SHAP analysis and dependence plots to identify key risk factors and interactions.

Main Results:

  • LightGBM demonstrated superior performance in predicting injury severity.
  • Key factors influencing crash severity include: number of vehicles, impact point, rider gender, lighting, pedestrian crossings, and road types.
  • Male riders, low-light conditions, higher speed limits, single-carriageways, and wet surfaces correlate with increased severe injury likelihood.

Conclusions:

  • Findings highlight critical factors contributing to e-scooter crash severity.
  • Recommendations are proposed to improve e-scooter rider safety based on identified risks.