Scalable methods to collect and visualize sidewalk accessibility data for people with mobility impairments

Abstract

Poorly maintained sidewalks pose considerable accessibility challenges for people with mobility impairments. Despite comprehensive civil rights legislation of Americans with Disabilities Act, 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, my Ph.D. dissertation introduces and evaluates new scalable methods for collecting data about street-level accessibility using a combination of crowdsourcing, automated methods, and Google Street View (GSV). My dissertation has four research threads. First, we conduct a formative interview study to establish a better understanding of how people with mobility impairments currently assess accessibility in the built environment and the role of emerging location-based technologies therein. The study uncovers the existing methods for assessing accessibility of physical environment and identify useful features of future assistive technologies. Second, we develop and evaluate scalable crowdsourced accessibility data collection methods. We show that paid crowd workers recruited from an online labor marketplace can find and label accessibility attributes in GSV with accuracy of 81%. This accuracy improves to 93% with quality control mechanisms such as majority vote. Third, we design a system that combines crowdsourcing and automated methods to increase data collection efficiency. Our work shows that by combining crowdsourcing and automated methods, we can increase data collection efficiency by 13% without sacrificing accuracy. Fourth, we develop and deploy a web tool that lets volunteers to help us collect the street-level accessibility data from Washington, D.C.As of writing this dissertation, we have collected the accessibility data from 20% of the streets in D.C. We conduct a preliminary evaluation on how the said web tool is used.Finally, we implement proof-of-concept accessibility-aware applications with accessibility data collected with the help of volunteers.My dissertation contributes to the accessibility, computer science, and HCI communities by: (i) extending the knowledge of how people with mobility impairments interact with technology to navigate in cities; (ii) introducing the first work that demonstrates that GSV is a viable source for learning about the accessibility of the physical world; (iii) introducing the first method that combines crowdsourcing and automated methods to remotely collect accessibility information; (iv) deploying interactive web tools that allow volunteers to help populate the largest dataset about street-level accessibility of the world; and (v) demonstrating accessibility-aware applications that empower people with mobility impairments.

Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks

“…One solution to this issue using information and communication technology is to develop an accessibility map as a large geographic information system (GIS) to provide accessibility information (Laakso et al 2011;Karimi et al 2014). In the existing methods for gathering large-scale accessibility information, experts evaluate sidewalk accessibilities from their images (Ponsard and Snoeck 2006), or accessibility information is recruited from volunteers by crowdsourcing (Hara 2014). These methods depend on human labor and are impractical when collecting accessibility information in a huge area.…”

Weakly Supervised Learning for Evaluating Road Surface Condition from Wheelchair Driving Data

“…To collect accessibility information, Ponsard and Snoeck proposed a method where experts evaluate accessibilities of sidewalks from their images for each case [4]. Crowdsourcing methods to recruit information from volunteers were proposed by Hara [5] and Cardonha et al [6]. These methods, however, depend on human labor and are impractical when collecting accessibility information in a huge area.…”

Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies

“…One method of solving these issues using information communication technology is to develop an accessibility map as a large geographic information system to provide the accessibility information [1][2][3]. The conventional method for gathering accessibility information on a large scale is as follows: a method for experts to evaluate sidewalks and their images for each case [4]; crowdsourcing methods to recruit information from volunteers [5,6]; and so on. In all these methods, human labor is indispensable.…”