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

Hara, Kotaro; Azenkot, Shiri; Campbell, Megan; Bennett, Cynthia L.; Le, Vicki; Pannella, Sean; Moore, Robert J.; Minckler, Kelly; Ng, Rochelle H.; Froehlich, Jon E.

doi:10.1145/2513383.2513448

Cited by 32 publications

(45 citation statements)

References 24 publications

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“…First, to validate the use of GSV imagery as a reliable source of curb ramp knowledge, we conducted physical audits in two of these cities and compared our results to GSV-based audit data. As with previous work exploring the concordance between GSV and the physical world 113 [11,39,73,77,157], we found high correspondence between the virtual and physical audit data. Second, we evaluated Tohme's performance in detecting curb ramps across our entire dataset with 403 turkers.…”

Section: Introductionsupporting

confidence: 88%

“…GSV has been validated as a useful dataset for a range of foci within the built environment. Our work reinforced these finding by showing that GSV is a good data source for built accessibility features like presence and absence of curb ramps [77,78,84].…”

Section: Virtual Street Audit Using Google Street Viewsupporting

confidence: 69%

“…Previous work has examined how to leverage massive online map datasets such as GSV along with crowdsourcing to collect information about the accessibility of the built environment [73,77,80,81,82]. Early results have been promising; for example, using a manually curated set of static GSV images, we found that minimally trained crowd workers in Amazon Mechanical Turk (turkers) could find four types of street-level accessibility problems with 81% accuracy [81].…”

Section: Introductionmentioning

confidence: 90%

“…Though prior work, including ours described in the following chapters, has explored crowdsourced or semiautomated methods to remotely collect outdoor accessibility information at scale [3,73,77,81,84], these methods rely on potentially out-of-date information, offer no way for users to update or comment, and do not yet work for indoor environments.…”

Section: Future Workmentioning

confidence: 99%

“…This study addresses each of these points. Multiple studies have previously demonstrated high concordance between GSV-based audits and audits conducted in the physical world [11,39,73,77]; however, prior work has not examined curb ramps specifically. Though this audit study was labor intensive, it is important to establish GSV as a reliable data source for curb ramp information, as it is the crux of our system's approach.…”

Section: Study 1: Assessing Gsv As a Data Sourcementioning

confidence: 99%

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

Hara

2015

SIGACCESS Access. Comput.

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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.

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Section: Introductionsupporting

confidence: 88%

Section: Virtual Street Audit Using Google Street Viewsupporting

confidence: 69%

Section: Introductionmentioning

confidence: 90%

Section: Future Workmentioning

confidence: 99%

Section: Study 1: Assessing Gsv As a Data Sourcementioning

confidence: 99%

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

Hara

2015

SIGACCESS Access. Comput.

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Smart Cities to Improve Mobility and Quality of Life of the Visually Impaired

Sobnath

Rehman

Nasralla

2019

EAI/Springer Innovations in Communication and Computing

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The rapid pace of innovation and advances in technological research has given hope to the visually impaired, to find ways to move around smart cities and have a better quality of life (QoL). There are around 110 million people suffering from visual impairments worldwide and research will continue to be adapted to find innovative solutions to provide a journey closer to total accessibility for the visually impaired. It has been identified through various studies that the requirements of people with visual impairment fall into two major categories. Firstly, the ability to recognise people, leading towards social interactions. Secondly, the ability to carry out routine activities seamlessly without any hindrance. Through the use of artific ia l intelligence, availability of data, high bandwidth, large number of connected devices and the collaboration of the citizens in a smart city, the life of the Visually Impaired Persons (VIPs) can be improved by providing them with more independence and safety. Moreover, smart cities also support the concept of sustainable economic growth as well as well-being of its citizens, therefore its development relies on strong ICT infrastructure. With a rise in smartphones, wearable devices, and the surge in the adoption of Artificial Intelligence (AI), Internet of Things (IoT), and Virtual and Augmented Reality (VR)/(AR) have provided aspiration for VIPs to lead a better QoL. A number of studies have already tested the use of these technologie s and have showed optimistic results. The main sectors that could be improved to cater for the visually impaired in smart cities are public areas, transportation systems, and the home systems. This chapter provides a comprehensive review and recommendations on how a smart city can provide a better QoL for the visually impaired in the near future.

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Experiments with a Public Transit Assistant for Blind Passengers

Flores

Manduchi

2016

Lecture Notes in Computer Science

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Abstract. Public transportation is key to independence for many blind persons. Unfortunately, in spite of recent accessibility progress, use of public transportation remains challenging without sight. In this contribution, we describe a system that provides enhanced travel-related information access to a blind bus passenger. Users of this system can select a specific bus line and desired destination on a regular Android smartphone or tablet; are notified when the bus arrives; once in the bus, they are informed of its progress during the route; and are given ample advance notice when the bus is approaching their destination. This system was tested with four blind participants in realistic conditions.

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

Cited by 32 publications

References 24 publications

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

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

Smart Cities to Improve Mobility and Quality of Life of the Visually Impaired

Experiments with a Public Transit Assistant for Blind Passengers

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