Combining Crowdsourcing and Machine Learning to Collect Sidewalk Accessibility Data at Scale
PI: Jon Froehlich (UW), jonf@cs.washington.edu, ORCID: 0000-0001-8291-3353
Co PIs: none
AMOUNT & MATCH: $50,000 from PacTrans; $50,000 Match
PERFORMANCE PERIOD: 8/16/2019 – 8/15/2021
STATUS: Completed
CATEGORIES: Sidewalks, Pedestrians, Accessibility, Crowdsourcing, Machine Learning
DESCRIPTION: Sidewalks significantly impact the mobility and quality of life of millions of Americans. In the proposal, we 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 our prior research, we 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 our tools.
While our proposed techniques and tools should work anywhere with OpenStreetMaps and available streetscape imagery (e.g., Google Street View, Mapillary), two of our three immediate deployment targets are cities in the PacTrans region: Newberg, OR and Seattle, WA.
DELIVERABLE | DUE DATE | DATE RECEIVED |
Research Project Progress Report #1 | 4/10/2020 | 4/10/2020 |
Research Project Progress Report #2 | 10/10/2020 | 10/28/2020 |
Research Project Progress Report #3 | 4/10/2021 | N/A |
No Cost Extension Request | 6/15/2021 | N/A |
Draft Report | 6/15/2021 | 7/28/2021 |
Final Project Report | 8/15/2021 | 10/12/2021 |