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(MTA) has partnered with Google for a groundbreaking pilot project designed to enhance the dependability of its outdated subway network.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.
Beginning in September 2024 and wrapping up in January 2025, the pilot project involved equipping certain subway cars with Google Pixel smartphones. These phones were responsible for gathering sound and vibration information to identify possible track issues. This data was subsequently evaluated by Google’s AI systems in the cloud, which identified zones that needed further examination by MTA staff.
“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.
The collaboration between the MTA and Google forms a component of a larger initiative to update New York City’s 120-year-old subway system, which still confronts issues due to its outdated infrastructure and regular delays. Although the pilot program yielded encouraging outcomes, doubts persist about the potential expansion of TrackInspect, considering the financial limitations the MTA is experiencing.
Addressing delays using 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.
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, un asistente del jefe de vías de la MTA, desempeñó un papel crucial en el proyecto. Sus tareas incluían examinar los fragmentos de audio señalados por el sistema de inteligencia artificial para detectar posibles problemas en las vías. “El sistema destacó áreas con niveles de decibelios anormales, lo que podría sugerir uniones sueltas, rieles dañados, u otros defectos,” explicó Sarno.
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
Promising results but hurdles remain
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.
The program also included an AI-powered tool based on Google’s Gemini model, which allowed inspectors to ask questions about maintenance protocols and repair history. This conversational AI provided inspectors with clear, actionable insights, further streamlining the maintenance process.
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.
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.
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.
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.
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.
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