Shared Micromobility as a Last-Mile Complement to Public Transit

Shared micromobility services, especially dockless e-scooters and e-bike, have experienced explosive growth across cities in recent years. People in the US took 136 million trips on shared bikes, e-bikes, and e-scooters (86 million trips) in 2019, 60% more than in 2018. As this trend continues, it becomes increasingly important for transit agencies to understand and respond to the impacts of micromobility on transit ridership and operations; given that shared micromobility is best used for short trips, a strategic response is to promote shared micromobility as a last-mile complement to public transit. Integrating micromobility with transit can not only enhance customer experience but also promote transit ridership. Despite a growing research interest in this topic, several major knowledge gaps remain. First, we lack knowledge of the spatiotemporal patterns of integrated transit and shared micromobility. That is, we do not know when, where, and how the transit-micromobility integration happened at high levels of spatial and temporal resolution, which limits vendors’ and local administrations’ ability to monitor and intervene. Second, little work has examined the equity aspects of transit-micromobility integration. Since public transit disproportionately serves low-income communities and minority populations in the U.S., enhancing the integration between transit and shared micromobility may deliver significant equity benefits. However, there has been little empirical work on identifying strategies to promote this potential. The availability of high-resolution micromobility data (e.g., GBFS data) and transit data (e.g., GTFS static and real-time data) provides a great potential to fill in these research gaps and generate important insights to inform planning and policy decisions in both private and public sectors. The overall objective of this project is to explore strategies that can maximize the potential of leveraging shared micromobility to complement public transit. We will work with transportation agencies (e.g., District Department of Transportation and Washington Metropolitan Area Transit Authority) and industry partners (e.g., Spin, Lime, and Lyft) to examine three research questions: 1) Where are first-mile/last-mile transit-connecting shared e-scooters trips happening and what factors shape this use? 2) To what extent does transitmicromobility integration benefit traditionally marginalized communities and advance equity? To address the two research questions, we will leverage existing datasets such as GPS data and survey data and obtain high-resolution trip-level data from our collaborators from industry and local governments. UF has previously collaborated with Spin/Ford Mobility to explore bundled pricing strategies to promote shared e-scooters and transit integration. Specifically, we plan to undertake the following research tasks: (1) Conduct a literature review regarding the state of knowledge on transit and micromobility integration. (2) Collect relevant data. The PI has previously conducted a behavioral survey to analyze who are shared micromobility riders, who are using shared micromobility to connect with transit as well as what factors shape the frequency of this use. Our team has also obtained a unique and novel dataset from micromobility operator, Spin, that indicates the time and location of Spin shared e-scooter trips as well as whether the trip was a transit-connecting trip. The two datasets are complementary to each other as the former can shed light on who and why people are using shared micromobility to connect with transit and the latter can shed light on spatiotemporal patterns of transitintegrated shared micromobility trips. (3) Conduct exploratory analysis. We will analyze the survey data with univariate analysis and cross-tabulation analysis. We will also conduct geospatial analyses of the transit-connecting shared micromobility trip patterns, focusing on how they differ across neighborhoods of distinct sociodemographic characteristics. For example, we will examine if transit-connecting shared micromobility trips disproportionately happen in advantageous neighborhoods compared to traditionally underserved neighborhoods. (4) Perform statistical modeling. We expect to build two sets of statistical models. One is a set of travel behavior models that analyze which population groups are more inclined to adopt shared micromobility as a first-mile/last-mile transit solution and the factors that shape usage frequency. The other is a regression model (e.g., Possion or negative binomial) that examines which geospatial factors are associated with the frequency of transit-connecting shared micromobility trips. (5) Summarize findings. Results from Tasks 4) and 5) will be summarized into a final report which we expect to include actionable insights and policy recommendations for public agencies (DOTs, transit agencies) and private vendors regarding promoting transit-micromoblity integration. We plan to develop two journal manuscripts based on the proposed work.

• Supplemental Notes:
  • Funding: USDOT: $150,000 Matching: $75,000

Language

• English

Project

• Status: Active
• Funding: $225000
• Contract Numbers:

  69A3552348337

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Equitable Transit-Oriented Communities (CETOC)

  University of New Orleans
  New Orleans, LA  United States 
• Project Managers:

  Kline, Robin

  Tian, Guang

• Performing Organizations:

  University of Florida

  207 Grinter Hall
  PO Box 115500
  Gainesville, Florida  United States  32611
• Principal Investigators:

  Yan, Xiang Jacob

• Start Date: 20231001
• Expected Completion Date: 20241031
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Electric bicycles; Electric vehicles; Equity; First mile and last mile; Micromobility; Public transit; Scooters; Shared mobility; Vehicle sharing
• Subject Areas: Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting; Public Transportation; Society;

Filing Info

• Accession Number: 01900212
• Record Type: Research project
• Source Agency: Center for Equitable Transit-Oriented Communities (CETOC)
• Contract Numbers: 69A3552348337
• Files: RIP
• Created Date: Nov 20 2023 4:31PM