Case Study on the Traffic Collision Patterns of E-Scooter Riders

Das, Subasish; Hossain, Ahmed; Rahman, M. Ashifur; Sheykhfard, Abbas; Kutela, Boniphace

doi:10.1177/03611981231185770

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…Their contribution was valuable in outlining heterogeneity in the involved variables. Das et al (2023) [29] provided an in-depth look into traffic collision patterns of e-scooter users, highlighting risks associated with driving behaviors and possible collisions with motorists. The application of the Perceptual Cycle Model (PCM) allowed analyzing causal factors in detail, by providing crucial policy recommendations to enhance road safety.…”

Section: Introductionmentioning

confidence: 99%

Analysis of E-Scooter Crashes in the City of Bari

Longo,

Berloco,

Coropulis

et al. 2024

Infrastructures

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The remarkable impact that e-scooters have had on the transportation system drives research on this phenomenon. The widespread use of e-scooters also poses several new safety issues, which should be necessarily studied. The aim of this paper points in this direction, investigating the main contributing factors, causes, and patterns of recorded e-scooter crashes, considering also different crash types and severity, using the City of Bari (Italy) as a case study. The crash dataset based on police reports and referring to the period July 2020–November 2022 (i.e., the first period of e-scooter implementation in the City of Bari) was investigated. Crashes were clustered according to several variables. No fatal crashes occurred, even though crashes mostly resulted in injuries (70%). Considering road type, divided roads were found to be less safe than undivided ones, due to higher mean speeds than on other roads and to a less constrained e-scooter driving behavior. Calm (off-peak) daytime hours seem to lead to more frequent e-scooter crashes with respect to both peak and nighttime hours, even if the latter hours are associated with an increased severity. Once controlled for exposure, season, lighting conditions, and the private/sharing ratio do not seem influential. E-scooters are more prone to be involved in single-vehicle and pedestrian crashes at segments than other vehicles, but they show similar crash trends than other vehicles (i.e., angle crashes) at intersections. As emerged from traffic surveys, not all e-scooter users were found to use cycle paths. Combining this information with crash data, it seems that not using cycle paths is considerably less safe than using them. Besides engineering measures and policies, awareness campaigns should be promoted to elicit safe users’ behavior and to tackle the several violations and misbehaviors emerging from the crash data.

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Section: Introductionmentioning

confidence: 99%