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Anyone who has rode the New York City subway can tell you that it has a lot of problems, from strange noises to flammable debris on the tracks. Now, as is the solution for everything these days, the Metropolitan Transportation Authority (MTA) is testing how AI could improve the repair process with the help of six Google Pixel phones.
In this case, the Google Pixel phones rode on four different subway cars between last September and January. The experiment, conducted in partnership with Google Public Sector, used the phone's accelerometers, magnetometers and microphones to pick up on any worrisome noises. This data was thn sent to cloud-based systems that generated predictive insights using machine learning algorithms.
The tech, known by Google as TrackInspect, found 92 percent of the defect locations that inspectors located. "By being able to detect early defects in the rails, it saves not just money but also time — for both crew members and riders" New York City Transit President Demetrius Crichlow stated in a release. "This innovative program — which is the first of its kind — uses AI technology to not only make the ride smoother for customers but also make track inspector’s jobs safer by equipping them with more advanced tools."
Typically, inspectors walk all 665 miles of the subway tracks to find any issues, along with sensor-laden “train geometry cars" picking up data three times a year. During the experiment, inspectors checked out any locations highlighted and confirmed whether there was a defect. They could also ask questions about maintenance and protocols through the tools generative AI system.
This article originally appeared on Engadget at https://www.engadget.com/ai/mta-str...-to-spot-track-defects-133046252.html?src=rss
In this case, the Google Pixel phones rode on four different subway cars between last September and January. The experiment, conducted in partnership with Google Public Sector, used the phone's accelerometers, magnetometers and microphones to pick up on any worrisome noises. This data was thn sent to cloud-based systems that generated predictive insights using machine learning algorithms.
The tech, known by Google as TrackInspect, found 92 percent of the defect locations that inspectors located. "By being able to detect early defects in the rails, it saves not just money but also time — for both crew members and riders" New York City Transit President Demetrius Crichlow stated in a release. "This innovative program — which is the first of its kind — uses AI technology to not only make the ride smoother for customers but also make track inspector’s jobs safer by equipping them with more advanced tools."
Typically, inspectors walk all 665 miles of the subway tracks to find any issues, along with sensor-laden “train geometry cars" picking up data three times a year. During the experiment, inspectors checked out any locations highlighted and confirmed whether there was a defect. They could also ask questions about maintenance and protocols through the tools generative AI system.
This article originally appeared on Engadget at https://www.engadget.com/ai/mta-str...-to-spot-track-defects-133046252.html?src=rss
