MTA launches pilot with Google Public Sector to detect track defects before they become operational issues

The Metropolitan Transportation Authority (MTA) has launched a pilot program in partnership with Google Public Sector to proactively detect potential track defects before they escalate into operational issues that disrupt service to customers. The pilot builds off the success of Google Public Sector’s TrackInspect prototype. 

The TrackInspect prototype, developed in partnership with the Rapid Innovation Team at Google Public Sector, integrates sensor hardware with advanced cloud and artificial intelligence (AI) capabilities to detect potential track issues. MTA notes that through the program, Google Pixel smartphones with standard, off-the-shelf plastic cases were retrofitted onto R46 subway cars on the A Line to capture subtle vibrations and sound patterns through built-in sensors equipped with an attached microphone, signaling the need for preventive maintenance. 

MTA says the sound and vibration data is sent in real time to cloud-based systems, where AI and machine learning algorithms generate predictive insights. According to the authority, New York City (NYC) Transit track inspectors serve as “humans in the loop,” inspecting locations highlighted by the system and confirming whether there is an issue, providing feedback to continuously train the model. TrackInspect also utilizes generative AI for natural language processing, allowing inspectors to ask questions about maintenance history, protocols and repair standards, with clear, conversational answers. 

“By being able to detect early defects in the rails, it saves not just money but also time – for both crew members and riders,” said NYC Transit President Demetrius Crichlow. “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.” 

“The TrackInspect pilot is a game-changer for the MTA, combining advanced cloud, AI and real-time sensor technology to transform how we maintain and monitor our subway infrastructure,” said MTA Chief Technology Officer Raf Portnoy. “It reflects our commitment to uniting technology and operations to drive innovation and safety.” 

MTA notes that in the initial pilot, TrackInspect collected 335 million sensor readings, one million GPS locations and 1,200 hours of audio. The data was combined with NYC Transit’s database of track non-conformities and ingested into a machine learning model running on Google Cloud. 

The authority says the data provided by the TrackInspect prototype complements the significant amount of information provided by the MTA’s track geometry cars. MTA notes that when used together, the technologies are a cost-effective way to make the track repair process faster and more accurate by finding and diagnosing potential track problems, causing fewer train delays and smoother service for riders. 

TrackInspect began as a proof-of-concept prototype developed by Google Public Sector exclusively for the MTA at no cost to the authority. 

"How it works is the prototype sends a soundbite or noise clip showing heavy vibration or noise, and then our inspectors follow up by walking the track and verifying any issue found,” said NYC Transit Department of Subways Assistant Chief Track Officer Robert Sarno. “We then compare that with whatever we find to teach the device noise and decibel levels and then work from there. That's how we are able to instruct the prototype on what are normal sounds and vibrations, and what are not and move along through the process.”   

“As more agencies adopt generative AI, Google Public Sector is excited to partner with innovative government leaders, like the leadership team at the Metropolitan Transportation Authority,” said Google Public Sector Vice President, Go-to-Market, Brent Mitchell. “The TrackInspect pilot program identified 92 percent of the defect locations found by track inspectors, illustrating that enhanced data analysis can help expedite problem identification and resolution to improve railway reliability."  

Transforming subway maintenance for the future 

MTA says its vision for the future includes scaling AI-driven track inspections across the entire subway system, enhancing data-sharing and collaboration between maintenance teams and AI systems, and leveraging real-time insights to reduce unplanned service disruptions. 

In parallel with advancing this pilot program, the MTA released a request for expressions of interest for other companies who have developed sensors or analytical capabilities that can “plug in” to the pilot.