The Metropolitan Transportation Authority (MTA) in New York City has partnered with Google for a groundbreaking pilot program focused on enhancing the reliability of its old subway network. Utilizing Google’s mobile technology, the effort aims to detect and resolve rail problems before they cause service interruptions. Named “TrackInspect,” the project signifies a considerable advancement in applying artificial intelligence and contemporary technology to public transportation.
La iniciativa piloto, que inició en septiembre de 2024 y finalizó en enero de 2025, consistió en equipar algunos vagones del metro con teléfonos Google Pixel. Estos dispositivos se encargaron de recolectar datos de audio y vibración para identificar posibles fallas en las vías. Luego, la información fue evaluada a través de los sistemas de inteligencia artificial en la nube de Google, los cuales señalaban las zonas que necesitaban una revisión más detallada por parte del personal de la MTA.
The pilot project, which began in September 2024 and concluded in January 2025, involved installing Google Pixel smartphones on select subway cars. These devices were tasked with collecting audio and vibration data to detect potential track defects. The data was then analyzed using Google’s cloud-based AI systems, which flagged areas requiring closer inspection by MTA personnel.
The collaboration between the MTA and Google forms part of a wider initiative to update New York’s 120-year-old subway network, which still struggles with issues tied to its outdated infrastructure and regular delays. Although the pilot program showed encouraging outcomes, uncertainties persist regarding the potential expansion of TrackInspect due to the MTA’s budgetary limitations.
Addressing delays through AI and smartphones
New York City’s commuters frequently encounter subway delays as a recurring issue. Towards the end of 2024, the MTA disclosed that tens of thousands of delays were occurring monthly, with December alone surpassing 40,000 incidents. These interruptions stem from multiple causes, such as track problems, construction activities, and crew shortages.
The TrackInspect initiative focuses on tackling a crucial element of the problem: pinpointing and correcting mechanical issues before they worsen. Throughout the pilot phase, six Google Pixel smartphones were placed in four R46 subway cars, recognizable by their unique orange and yellow seats. These devices captured 335 million sensor readings, more than one million GPS points, and 1,200 hours of audio data.
The smartphones were strategically located both inside and beneath the subway cars. The external devices were fitted with microphones to record both sound and vibrations, whereas the internal phones had their microphones deactivated to ensure passenger conversations weren’t recorded. These internal devices focused exclusively on capturing vibrations to identify any irregularities in the tracks.
Rob Sarno, serving as an assistant chief track officer for the MTA, was integral to the project. His duties involved examining audio clips that the AI system flagged for potential track issues. “The system pinpoints zones with unusual decibel levels, possibly signaling loose joints, damaged rails, or other defects,” Sarno elaborated.
La línea de tren A, seleccionada para el piloto, presentó un entorno de prueba variado con vías tanto subterráneas como elevadas. Además, incluyó segmentos de infraestructura recientemente construida, ofreciendo un punto de referencia para comparaciones. Aunque no todos los retrasos en la línea A se deben a problemas mecánicos, los datos recopilados durante el programa piloto podrían contribuir a resolver problemas recurrentes y mejorar el servicio en general.
The A train line, chosen for the pilot, offered a diverse testing environment with both underground and above-ground tracks. It also included sections of recently constructed infrastructure, providing a baseline for comparison. While not all delays on the A line are caused by mechanical issues, the data captured during the pilot could help address recurring problems and improve overall service.
Promising results but hurdles remain
El programa también incorporó una herramienta impulsada por inteligencia artificial basada en el modelo Gemini de Google, que permitía a los inspectores hacer preguntas sobre protocolos de mantenimiento e historial de reparaciones. Esta inteligencia artificial conversacional ofrecía a los inspectores información clara y útil, lo que facilitaba aún más el proceso de mantenimiento.
Despite its achievements, the pilot program brings up questions concerning its scalability and expenses. The MTA has not revealed the potential cost of deploying TrackInspect throughout its entire subway network, which comprises 472 stations and accommodates over one billion riders each year. The agency is also facing financial difficulties, requiring billions of dollars to finish ongoing infrastructure projects.
Despite its success, the pilot program raises questions about scalability and cost. The MTA has not disclosed how much it would cost to implement TrackInspect across its entire subway system, which includes 472 stations and serves over one billion riders annually. The agency is already grappling with financial challenges, needing billions of dollars to complete existing infrastructure projects.
Google’s involvement in the pilot was part of a proof-of-concept initiative developed at no cost to the MTA. However, expanding the program would likely require significant investment, making funding a major consideration for decision-makers.
A growing trend in transit innovation
Google has previously worked with other transportation agencies. The tech company has created tools to optimize Amtrak’s scheduling and has teamed up with parking technology providers to incorporate street parking information into Google Maps. Nonetheless, the size and intricacy of New York’s subway system make this project especially ambitious.
The MTA operates the largest subway network in the United States, offering 24-hour service on numerous lines. This continuous operation introduces additional complexity to maintenance tasks, as repairs and upgrades frequently have to be performed alongside active service. Employing AI and smartphone technology, the TrackInspect program might assist the MTA in tackling these challenges more effectively.
Future Prospects
Aunque el piloto de TrackInspect ha concluido, la MTA está investigando asociaciones con otros proveedores de tecnología para seguir mejorando sus procesos de mantenimiento. La agencia también está evaluando los datos del piloto para determinar su impacto en la reducción de retrasos y mejora del servicio. Las primeras señales sugieren que ciertos tipos de retrasos, como los causados por problemas de frenado y defectos en las vías, disminuyeron en la línea A durante el periodo del piloto. No obstante, la MTA advierte que se requiere un análisis más detallado para confirmar un vínculo directo con el programa.
Por el momento, el piloto simboliza un paso esperanzador hacia la modernización de las operaciones de la MTA y la resolución de los desafíos de un sistema de tránsito envejecido. Al combinar el conocimiento de empresas tecnológicas como Google con la experiencia de los profesionales del transporte, la ciudad de Nueva York podría ofrecer una experiencia de metro más confiable para sus millones de pasajeros diarios.
Reflecting on the project, Sarno highlights the promise of AI-driven solutions to revolutionize public transit. “This technology enables us to identify issues sooner, act more swiftly, and ultimately offer improved service to our passengers,” he stated.
As Sarno reflects on the project, he emphasizes the potential of AI-driven solutions to transform public transportation. “This technology allows us to detect problems earlier, respond faster, and ultimately provide better service to our customers,” he said.
The MTA’s collaboration with Google underscores the potential of public-private partnerships to drive innovation in critical infrastructure. Whether TrackInspect becomes a permanent fixture in New York’s subway system remains to be seen, but its success highlights the possibilities of integrating cutting-edge technology into the daily lives of commuters.
