Unlocking the role of shared dockless e-scooters bridging last-mile gaps: A quasi-experimental study of metro rail transit in Los Angeles

One major challenge of a public transit system is first- and last-mile (FLM) connectivity. With the advent of smart technology and on-demand transportation services, shared micromobility is believed to provide a low-cost solution for bridging the first- and last-mile gap. However, several studies have arrived at mixed conclusions about the FLM achieved by shared mircomobility. This study explores the causal effect of shared dockless e-scooters on last-mile connectivity to public transit by controlling confounding variables, including built environment and socioeconomic status. The study employs a quasi-experimental design. This study defines last-mile connectivity, here, treatment effect, if e-scooter service areas near rail stations increase metro rail ridership after the introduction of e-scooter service. After matching pairs between treatment and control groups using propensity score matching, we use difference-in-difference regression to examine the causal relationship between shared dockless e-scooter service and ridership changes in public rail transit before and after e-scooter service. The findings indicate that the treatment effect, a higher e-scooter trip density around transit buffer areas, positively impacts monthly rail ridership. In conclusion, planning and transportation agencies can develop street design guidelines or transit-oriented development to encourage the active and safe use of e-scooters, thereby promoting better integration of shared dockless e-scooters with transit systems.

• Record URL:
  • https://doi.org/10.1016/j.tbs.2024.100869
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2214367X24001327
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/2214367X
• Supplemental Notes:
  • © 2024 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yang, Wookjae
  • 0000-0002-6305-3877
  • Ewing, Reid
  • 0000-0002-4117-3456
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 100869
• Serial:
  • Travel Behaviour and Society
  • Volume: 37
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2214-367X
  • Serial URL: http://www.sciencedirect.com/science/journal/2214367X

Subject/Index Terms

• TRT Terms: Electric vehicles; First mile and last mile; Scooters; Shared mobility
• Geographic Terms: Los Angeles (California)
• Subject Areas: Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01926097
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 30 2024 3:58PM