New York University’s (NYU) Tandon School of Engineering described in an online research brief that a new trip-planning app designed by researchers at NYU Tandon and NYU Grossman School of Medicine has shown encouraging results in improving navigation inside subway stations, which offers the possibility of easier commutes for people with blindness and low-vision. Findings from a study of the app were published by Junchi Feng, PhD (Candidate), et al in IEEE Journal of Translational Engineering in Health and Medicine.
According to the NYU Tandon article, theCommute Booster app routes public-transportation users through the “middle mile”—the part of a journey inside subway stations or other similar transit hubs—in addition to the “first” and “last” miles that bring travelers to and from those hubs.
The research team, which includes advisors from New York City’s Metropolitan Transit Authority, was led by John-Ross Rizzo, MD. Dr. Rizzo is an associate professor in NYU Tandon’s Biomedical Engineering department and is on the faculty of NYU Grossman. The research was supported by the National Science Foundation, the National Eye Institute, the Fogarty International Center, and the US Department of Defense.
“The ‘middle mile’ often involves negotiating a complex network of underground corridors, ticket booths, and subway platform—it can be treacherous for people who cannot rely on sight,” commented Dr. Rizzo in the NYU Tandon research brief. “Most GPS-enabled navigation apps address ‘first’ and ‘last’ miles only, so they fall short of meeting the needs of blind or low-vision commuters. Commute Booster is meant to fill that gap.”
Additionally, the article noted, subway signs are typically graphical or text-based, creating challenges for the visually impaired to recognize from a distance, thereby reducing their ability to be autonomous in unfamiliar environments.
Commute Booster is designed to automatically determine what signs a traveler will encounter along the way to a specific subway platform. Then, it uses a smartphone’s camera to recognize and interpret signs posted inside transit hubs, while ignoring irrelevant ones and prompting users to follow relevant ones only.
In the study, the Commute Booster app demonstrated 97% accuracy in identifying signs relevant to reach the intended destination. Additionally, Commute Booster could “read” signs from distances and at angles that reflect expected physical positioning of travelers.
The Commute Booster system relies on two technological components.
The first is general transit feed specification (GTFS), a standardized way for public transportation agencies to share their transit data with developers and third-party applications. The second is optical character recognition (OCR) technology that can translate images of text into actual editable text.
The GTFS dataset contains descriptions for locations and pathways within each subway station. Commute Booster’s algorithm uses this information to generate a comprehensive list of wayfinding signage within subway stations that users would encounter during their intended journey. The OCR functionality reads all texts presented to users in their immediate surroundings. Commute Booster’s algorithm can identify relevant navigation signs and locate the position of signs in the immediate environments.
By integrating these two components, Commute Booster provides real-time feedback to users regarding the presence or absence of relevant navigation signs within the field of view of their phone camera during their journey.
An additional study of Commute Booster will be conducted in the near future. The app could be available for public use in the near term, noted the NYU Tandon article.
