Characterizing City Accessibility at Scale

Characterizing City Accessibility at Scale

Poorly maintained sidewalks pose considerable accessibility challenges for people with mobility impairments. Despite comprehensive civil rights legislation for Americans with disabilities, many city streets and sidewalks in the U.S. remain inaccessible. The problem is not just that sidewalk accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori.

To address this problem, we introduce new scalable methods for collecting data about street-level accessibility using a combination of crowdsourcing, automated methods, and Google Street View as well as proof-of-concept map-based accessibility applications that leverage this data.

Related Publications

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A Pilot Deployment of an Online Tool for Large-Scale Virtual Auditing of Urban Accessibility
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The Design of Assistive Location-based Technologies for People with Ambulatory Disabilities: A Formative Study
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Improving Public Transit Accessibility for Blind Riders by Crowdsourcing Bus Stop Landmark Locations with Google Street View: An Extended Analysis
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Tohme: Detecting Curb Ramps in Google Street View Using Crowdsourcing, Computer Vision, and Machine Learning
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An Initial Study of Automatic Curb Ramp Detection with Crowdsourced Verification using Google Street View Images
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Improving Public Transit Accessibility for Blind Riders by Crowdsourcing Bus Stop Landmark Locations with Google Street View
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Combining Crowdsourcing and Google Street View to Identify Street-level Accessibility Problems
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A Feasibility Study of Crowdsourcing and Google Street View to Determine Sidewalk Accessibility