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.
“By spotting initial indicators of track deterioration, we not only cut down on maintenance expenses but also lessen inconveniences for passengers,” stated Demetrius Crichlow, president of New York City Transit, in an announcement made public in late February.
“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.
Addressing delays through AI and smartphones
Subway delays continue to be a constant issue for those traveling in New York City. Towards the end of 2024, the MTA documented tens of thousands of delays monthly, with numbers surpassing 40,000 in just December. These interruptions stem from numerous causes, such as track flaws, construction activities, and shortages of crew members.
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.
Rob Sarno, an assistant chief track officer with the MTA, played a key role in the project. His responsibilities included reviewing audio clips flagged by the AI system to identify potential track issues. “The system highlighted areas with abnormal decibel levels, which could indicate loose joints, damaged rails, or other defects,” Sarno explained.
Encouraging outcomes, yet challenges persist
The TrackInspect initiative produced promising results, as the AI system accurately identified 92% of defect locations that were confirmed by MTA inspectors. Sarno estimated his own accuracy rate in anticipating track defects from audio data to be approximately 80%.
The initiative also featured an AI-driven tool based on Google’s Gemini model, enabling inspectors to inquire about maintenance procedures and repair records. This conversational AI furnished inspectors with straightforward, actionable insights, which further streamlined the maintenance workflow.
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.
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.
La red de metro de la MTA es la más grande de Estados Unidos, brindando servicio las 24 horas en muchas de sus líneas. Este funcionamiento continuo añade otra capa de complejidad a los esfuerzos de mantenimiento, ya que las reparaciones y mejoras a menudo deben realizarse junto al servicio activo. Con el uso de tecnología de inteligencia artificial y teléfonos inteligentes, el programa TrackInspect podría ayudar a la MTA a enfrentar estos desafíos de manera más eficiente.
Looking forward
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.
While the TrackInspect pilot has ended, the MTA is exploring partnerships with other technology providers to further enhance its maintenance processes. The agency is also analyzing data from the pilot to determine its impact on reducing delays and improving service. Early indications suggest that certain types of delays, such as those caused by braking issues and track defects, decreased on the A line during the pilot period. However, the MTA cautions that further analysis is needed to confirm a direct link to the program.
For now, the pilot represents a promising step toward modernizing the MTA’s operations and addressing the challenges of an aging transit system. By combining the expertise of tech companies like Google with the experience of transit professionals, New York City may be able to deliver a more reliable subway experience for its millions of daily riders.
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.
