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.
“In recognizing the initial indicators of track deterioration, we not only decrease maintenance expenses but also lessen disruptions experienced by passengers,” stated Demetrius Crichlow, the president of New York City Transit, in a statement issued in late February.
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of a wider initiative to update New York’s 120-year-old subway network, which still struggles with issues tied to its outdated infrastructure and regular delays.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.
Subway delays are a persistent problem for New York City commuters. In late 2024, the MTA reported tens of thousands of delays each month, with figures exceeding 40,000 in December alone. These disruptions are caused by a variety of factors, including track defects, construction, and crew shortages.
Los teléfonos inteligentes se colocaron estratégicamente tanto dentro como debajo de los vagones del metro. Los dispositivos externos estaban equipados con micrófonos para captar sonidos y vibraciones, mientras que los internos tenían los micrófonos desactivados para evitar grabar conversaciones de los pasajeros. En cambio, estos dispositivos se concentraban únicamente en las vibraciones para identificar anomalías en las vías.
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.
The A train line was selected for the pilot, providing a varied testing environment with both subterranean and elevated tracks. It also featured segments of newly built infrastructure, which served as a benchmark for analysis. Although not every delay on the A line is due to mechanical issues, the data gathered during the pilot could assist in resolving persistent problems and enhancing overall service.
Encouraging outcomes, yet challenges persist
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.
A pesar de su éxito, el programa piloto plantea dudas sobre su escalabilidad y coste. La MTA no ha revelado cuánto costaría implementar TrackInspect en todo su sistema de metro, que abarca 472 estaciones y atiende a más de mil millones de pasajeros cada año. La agencia ya se enfrenta a desafíos financieros, necesitando miles de millones de dólares para completar proyectos de infraestructura en curso.
La participación de Google en el piloto formó parte de una iniciativa de prueba de concepto desarrollada sin costo para la MTA. Sin embargo, ampliar el programa probablemente requeriría una inversión considerable, convirtiendo el financiamiento en un factor clave para los responsables de la toma de decisiones.
An increasing trend in transit advancement
New York’s collaboration with Google is part of a wider movement where cities around the globe are utilizing artificial intelligence and smart technologies to enhance public transit systems. For instance, New Jersey Transit has employed AI to study passenger flow and manage crowds, while the Chicago Transit Authority has established AI-based security systems to identify weapons. In Beijing, facial recognition technology has been adopted as an alternative to conventional transit tickets, minimizing wait times during busy hours.
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.
Google itself has collaborated with other transportation agencies in the past. The tech giant has developed tools to enhance Amtrak’s scheduling and partnered with parking technology providers to integrate street parking data into Google Maps. However, the scale and complexity of New York’s subway system make this project particularly ambitious.
The MTA’s subway network is the largest in the United States, with 24-hour service on many lines. This round-the-clock operation adds another layer of complexity to maintenance efforts, as repairs and upgrades often need to be conducted alongside active service. By using AI and smartphone technology, the TrackInspect program could help the MTA address these challenges more efficiently.
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.
Currently, the pilot serves as an encouraging move toward updating the MTA’s operations and tackling the difficulties of an outdated transit system. By merging the knowledge of tech firms like Google with the expertise of transit professionals, New York City could potentially provide a more dependable subway experience for its millions of daily passengers.
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.
