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Electric scooters (or e-scooters) are among the most popular micromobility options that have experienced an enormous expansion in urban transportation systems across the world in recent years. Along with the increased usage of e-scooters, the increasing number of e-scooter-related injuries has also become an emerging global public health concern. However, little is known regarding the risk factors for e-scooter-related crashes and injury crashes. This study consisted of a two-phase survey questionnaire administered to a cohort of e-scooter riders (n = 210), which obtained exposure information on riders’ demographics, riding behaviors (including infrastructure selection), helmet use, and other crash-related factors. The risk ratios of riders’ self-reported involvement in an e-scooter-related crash (i.e., any crash versus no crash) and injury crash (i.e., injury crash versus non-injury crash) were estimated across exposure subcategories using the Negative Binomial regression approach. Males and frequent users of e-scooters were associated with an increased risk of e-scooter-related crashes of any type. For the e-scooter-related injury crashes, more frequently riding on bike lanes (i.e., greater than 25% of the time), either protected or unprotected, was identified as a protective factor. E-scooter-related injury crashes were more likely to occur among females, who reported riding on sidewalks and non-paved surfaces more frequently. The study may help inform public policy regarding e-scooter legislation and prioritize efforts to establish suitable road infrastructure for improved e-scooter riding safety.
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This content will become publicly available on July 1, 2025
Dissecting shared e-scooters usage patterns and its impact on other transportation modes: A case study of Portland city
The study analyzed the survey data from the 2018 Portland E-scooter Pilot Program and aims to determine (i) who uses shared e-scooters and why they use them, and (ii) whether there is any association between e-scooter usage and the usage of other modes of transportation. To accomplish the first objective, the study identifies the users of shared e-scooters based on their travel behavior using an unsupervised machine learning approach, latent class analysis (LCA). The LCA model grouped e-scooter users into three distinct classes: Class 1 (Recreational Enthusiasts) −occasional and frequent users for recreation, Class 2 (Commute Riders) −frequent users for work, and Class 3 (Intermittent Joyriders) −occasional and one-time users for recreation. Furthermore, a set of ordered logit models is employed to determine the second objective based on the identified classes of e-scooter users, their socio-demographic characteristics, and the built environment variables. The results of ordered logit models revealed that compared to Commute Riders, both Recreational Enthusiasts and Intermittent Joyriders exhibit less interest in increasing the usage of available transportation modes after adopting e-scooters. Notably, low-income e-scooter users show a higher probability of increasing their usage across various transportation modes, including public transportation, driving, shared mobility services, personal bikes, shared bikes, and walking. The study offers valuable insights to guide city planners and policymakers in developing effective strategies for the deployment of e-scooters, targeting each group of users.
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- Award ID(s):
- 2133379
- PAR ID:
- 10556642
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Travel Behaviour and Society
- Volume:
- 36
- Issue:
- C
- ISSN:
- 2214-367X
- Page Range / eLocation ID:
- 100812
- Subject(s) / Keyword(s):
- E-scootersShared micromobilityUser SegmentationLatent Class AnalysisOrdered Logit Model
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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