The New York Metropolitan Transportation Authority (MTA) is partnering with Google Public Sector to start a pilot program built upon the success of the TrackInspect prototype. The pilot will proactively detect potential track defects before they escalate into operational issues.
The Original TrackInspect Program
The TrackInspect prototype was developed in partnership with the Rapid Innovation Team at Google Public Sector and integrates sensor hardware with advanced cloud and artificial intelligence (AI) capabilities to detect potential track issues.
Through TrackInspect, Google Pixel smartphones were retrofitted onto R64 subway cars on the A line to capture subtle vibrations and sound patterns through built-in sensors equipped with an attached microphone that signal preventive maintenance is needed.
The data is sent in real-time to cloud-based systems where AI and machine learning algorithms generate predictive insights.
New York City Transit (NYCT) track inspectors serve as “humans in the loop,” inspecting locations highlighted by the system and confirming whether there is an issue while providing feedback to train the model.
TrackInspect also utilizes Generative AI for natural language processing, allowing inspectors to ask questions about maintenance history, protocols, and repair standards.
“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 New York City 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.”
Putting the Data to Use
In the initial pilot, Track Inspect collected 335 million sensor readings, one million GPS locations, and 1,200 hours of audio.
The data was combined with the NYCT’s track non-conformity database and ingested into a machine learning model running on Google Cloud.
TrackInspect’s data complements the significant amount of information provided by the MTA’s track Geometry cars. These technologies make the track repair process faster and more accurate by finding and diagnosing potential track problems.
Finding and fixing track issues faster means fewer train delays and smoother service for millions of daily riders.
TrackInspect began as a proof-of-concept prototype developed by Google Public Sector exclusively for the MTA at no cost to the Authority.
By investing in predictive maintenance and AI-driven solutions, the MTA is taking a major step toward modernizing its operations and ensuring the long-term sustainability of its subway network.
The agency’s 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.
MTA’s commitment to embracing innovation reflects its ongoing efforts to improve service, enhance safety, and optimize operational efficiency.
