Combining Crowdsourcing and Machine Learning to Collect Sidewalk Accessibility Data at Scale

Sidewalks significantly impact the mobility and quality of life of millions of Americans. In the proposal, the research team described new, scalable methods for collecting data on sidewalk accessibility using machine learning, crowdsourcing, and online map imagery as well as new interactive visualizations aimed at providing novel insights into urban accessibility. As with the team's prior research, the team will work closely with key stakeholders, including local governments and transit departments, mobility-impaired individuals and caretakers, and walkability advocates to help shape and evaluate the design of the team's tools.

    Language

    • English

    Project

    • Status: Active
    • Funding: $100000
    • Contract Numbers:

      69 A3551747110

    • Sponsor Organizations:

      United States Department of Transportation - FHWA - LTAP

      1200 New Jersey Avenue, SE
      Washington, DC    20590

      Office of the Assistant Secretary for Research and Technology

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

      University of Washington, Seattle

      433 Brooklyn Ave. NE
      Box 359472
      Seattle, WA  United States  98195-9472
    • Performing Organizations:

      University of Washington, Seattle

      433 Brooklyn Ave. NE
      Box 359472
      Seattle, WA  United States  98195-9472
    • Principal Investigators:

      Froehlich, Jon

    • Start Date: 20190916
    • Expected Completion Date: 20210915
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01723937
    • Record Type: Research project
    • Source Agency: Pacific Northwest Transportation Consortium
    • Contract Numbers: 69 A3551747110
    • Files: UTC, RiP
    • Created Date: Nov 27 2019 7:04PM