Spatial associations of dockless shared e-scooter usage

• Published in: Transportation research. Part D, Transport and environment Vol. 86; p. 102396
• Main Authors: Caspi, Or, Smart, Michael J., Noland, Robert B.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.09.2020
• Subjects: Geographically weighted regression; Micromobility; Shared e-scooters; Spatial analysis; Spatial analysis; Shared e-scooters; Geographically weighted regression; Micromobility
• ISSN: 1361-9209; 1879-2340
• DOI: 10.1016/j.trd.2020.102396

•Shared e-scooters have emerged as a transportation mode.•Using data from Austin, Texas, e-scooter usage patterns are analyzed.•Spatial Lag, Spatial Durbin and geographically weighted regressions are estimated.•Student populations are a major source of trips.•The impact of median income varies throughout the city. In this study, we explore the usage of e-scooter sharing services in Austin, Texas over about a six-month period. The study is based on trip records of all the shared e-scooter operators in Austin and includes trip start and end locations. We use both analysis of trip patterns and spatial regression techniques to examine how the built environment, land use, and demographics affect e-scooter trip generation. Our findings show that people use e-scooters almost exclusively in central Austin. Commuting does not seem to be the main trip purpose, and usage of e-scooters is associated with areas with high employment rates, and in areas with bicycle infrastructure. People use e-scooter sharing regardless of the affluence of the neighborhood, although less affluent areas with high usage rates have large student populations, suggesting that students use this mode of travel. Implications for planners suggest that better bicycle infrastructure will facilitate e-scooter usage, college towns are a ready market for e-scooter sharing services, and e-scooters may be a substitute for some short non-work trips, reducing car usage, and benefiting the environment.