Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with Google street view

• Main Authors: Kotaro HARA, AZENKOT, Shiri, CAMPBELL, Megan, BENNETT, Cynthia L., LE, Vicki, PANNELLA, Sean, MOORE, Robert, MINCKLER, Kelly, NG, Rochelle H., FROEHLICH, Jon E.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4006; https://ink.library.smu.edu.sg/context/sis_research/article/5008/viewcontent/Hara_ImprovingPublicTransitAccessibilityForBlindRidersByCrowdsourcingBusStopLandmarkLocationsWithGoogleStreetView_ASSETS2013.pdf
• Institution: Singapore Management University

Low-vision and blind bus riders often rely on known physicallandmarks to help locate and verify bus stop locations (e.g., bysearching for a shelter, bench, newspaper bin). However, there arecurrently few, if any, methods to determine this information apriori via computational tools or services. In this paper, weintroduce and evaluate a new scalable method for collecting busstop location and landmark descriptions by combining onlinecrowdsourcing and Google Street View (GSV). We conduct andreport on three studies in particular: (i) a formative interviewstudy of 18 people with visual impairments to inform the designof our crowdsourcing tool; (ii) a comparative study examiningdifferences between physical bus stop audit data and auditsconducted virtually with GSV; and (iii) an online study of 153crowd workers on Amazon Mechanical Turk to examine thefeasibility of crowdsourcing bus stop audits using our custom toolwith GSV. Our findings reemphasize the importance of landmarksin non-visual navigation, demonstrate that GSV is a viable busstop audit dataset, and show that minimally trained crowd workerscan find and identify bus stop landmarks with 82.5% accuracyacross 150 bus stop locations (87.3% with simple quality control).