Ubibus: Ubiquitous Computing to Help Blind People in Public Transport

Banâtre, Michel; Couderc, Paul; Pauty, Julien; Becus, Mathieu

doi:10.1007/978-3-540-28637-0_28

Cited by 24 publications

(15 citation statements)

References 3 publications

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“…Most transit tools designed to assist blind and low-vision bus riders focus on two issues: helping identify the correct bus to board when waiting at a bus stop [4,27] or providing alerts for an upcoming stop while riding the bus [21,22]. We are interested in addressing a prerequisite challenge: helping visually impaired riders find and verify bus stop locations through the use of physical landmarks and detailed bus stop descriptions (e.g., the presence of benches, bus shelters).…”

Section: Related Workmentioning

confidence: 99%

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

Hara

Azenkot

Campbell

et al. 2013

Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility

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Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for a shelter, bench, newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this paper, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies in particular: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool; (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV; and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in non-visual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5% accuracy across 150 bus stop locations (87.3% with simple quality control).

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Section: Related Workmentioning

confidence: 99%

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

Hara

Azenkot

Campbell

et al. 2013

Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility

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“…A more complex application is for example Ubi-Bus [4]: a blind person wanting to take a given bus line carries a smart object which acts as a typed token to stop the bus: when the token generated by the pedestrian is in the area of the bus stop, the latter requests the bus to stop as it approaches, by generating a red token as shown on figure 1. It is the spatial configuration of the pedestrian, the bus stop and the bus which controls the token generation and stop requests.…”

Section: Spatial Computingmentioning

confidence: 99%

A Spatial Computing Approach for Integrity Checking of Objects Groups

Banâtre¹,

Allard²,

Couderc³

2010

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop

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Integrity checking is important in many activities, such as logistic, telecommunication or even day to day tasks such as checking for someone missing in a group. While the computing and telecommunication worlds commonly use digital integrity checking, many activities from the real world do not beneficiate from automatic mechanisms for ensuring integrity. We propose a spatial computing approach where groups of physical objects tagged with RFID chips are similar to network packets and group integrity can be checked at relevant places.

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“…Previous studies have shown that people with visual impairment experience difficulties at determining the route and schedule information, purchasing fare, finding the correct bus-stop location, getting on the correct bus, and getting off at the right stop [23, 4,5,6,7] focused on identifying a correct bus to board when waiting at a bus stop, while the systems described in [8910] provided alerts for an upcoming stop while riding the bus. [11] proposed gathering spatial and temporal information from different patterns of mobility and travel time using smart card and GSM data.…”

Section: Related Workmentioning

confidence: 99%