The New York City Metropolitan Transportation Authority (MTA) has partnered with Google for a groundbreaking pilot project designed to enhance the dependability of its outdated subway network. Utilizing Google’s smartphone technology, this initiative aims to detect and resolve track problems proactively to prevent service interruptions. Called “TrackInspect,” the program marks a major advancement in incorporating artificial intelligence and contemporary technology into 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.
“By identifying early signs of track wear and tear, we not only reduce maintenance costs but also minimize disruptions for riders,” said Demetrius Crichlow, president of New York City Transit, in a statement released in late February.
The MTA’s partnership with Google is part of a broader effort to modernize New York’s 120-year-old subway system, which continues to face challenges related to aging infrastructure and frequent delays. While the pilot program demonstrated promising results, questions remain about whether TrackInspect will be expanded given the financial constraints facing the MTA.
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 TrackInspect program aims to address one critical aspect of the issue: identifying and resolving mechanical problems before they escalate. During the pilot, six Google Pixel smartphones were installed on four R46 subway cars, which are known for their distinctive orange and yellow seats. The devices recorded 335 million sensor readings, over one million GPS data points, and 1,200 hours of audio.
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
El programa TrackInspect produjo resultados alentadores, con el sistema de inteligencia artificial detectando con éxito el 92% de los lugares con defectos que fueron verificados por los inspectores de la MTA. Sarno calculó que su tasa de éxito personal al prever defectos en las vías basándose en datos de audio fue de aproximadamente un 80%.
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.
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.
La colaboración de Nueva York con Google forma parte de una tendencia más amplia en la que ciudades de todo el mundo están adoptando inteligencia artificial y tecnologías inteligentes para mejorar los sistemas de transporte público. Por ejemplo, New Jersey Transit ha utilizado IA para analizar el flujo de pasajeros y la gestión de multitudes, mientras que la Autoridad de Tránsito de Chicago ha implementado medidas de seguridad basadas en IA para detectar armas. En Pekín, se ha introducido la tecnología de reconocimiento facial como alternativa a los boletos de transporte tradicionales, disminuyendo los tiempos de espera en horas pico.
New York’s partnership with Google is part of a broader trend in which cities worldwide are adopting artificial intelligence and smart technologies to improve public transit systems. For example, New Jersey Transit has used AI to analyze passenger flow and crowd management, while the Chicago Transit Authority has implemented AI-driven security measures to detect weapons. In Beijing, facial recognition technology has been introduced as an alternative to traditional transit tickets, reducing wait times during peak hours.
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.
Future Prospects
Looking ahead
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.
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