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

Area of Science:

• Urban mobility and transportation safety
• Road safety engineering
• Geographic information systems (GIS) in transportation

Background:

• E-scooters are rapidly changing urban transport, but safety concerns are growing.
• Understanding e-scooter crash risks requires analyzing their interaction with the built environment.
• Existing research often overlooks the nuanced impact of diverse urban settings on e-scooter safety.

Purpose of the Study:

• To investigate the relationship between built environment characteristics and e-scooter crash severity in the UK.
• To develop a typology of built environments relevant to e-scooter usage.
• To identify specific risk factors and their varying impacts across different urban typologies.

Main Methods:

• Utilized UK e-scooter crash data and built environment data.
• Employed K-means++ clustering to categorize built environments.
• Applied a random parameter binary logit model to analyze crash severity within clusters.

Main Results:

• Identified four built environment clusters: car-centric/mixed-use, commercial/industrial, intersection-dense, and residential/central.
• Common risk factors include collisions with motor vehicles, younger riders, and higher speed limits.
• Riding on the carriageway significantly increased injury severity in car-centric and commercial/industrial zones.

Conclusions:

• The built environment is a critical factor in e-scooter crash severity.
• Unique contributing factors and varying risk magnitudes exist for each urban environment type.
• Tailored safety interventions considering specific built environments are necessary for e-scooter safety.