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(MTA) has partnered with Google for a groundbreaking pilot project designed to enhance the dependability of its outdated subway network.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.
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of a wider initiative to update New York’s 120-year-old subway network, which still struggles with issues tied to its outdated infrastructure and regular delays.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.
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.
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.
The smartphones were strategically placed both inside and underneath the subway cars. While the external devices were equipped with microphones to capture audio and vibrations, the internal phones had their microphones disabled to ensure passenger conversations were not recorded. Instead, these devices focused solely on vibrations to detect irregularities in the tracks.
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.
Encouraging outcomes, yet challenges persist
Promising results but hurdles remain
The TrackInspect program yielded encouraging results, with the AI system successfully identifying 92% of defect locations verified by MTA inspectors. Sarno estimated his personal success rate in predicting track defects based on audio data at around 80%.
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
A growing trend in transit innovation
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.
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.
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.
Although the TrackInspect pilot has concluded, the MTA is investigating collaborations with additional technology providers to further improve its maintenance procedures. The agency is also evaluating data from the pilot to assess its effects on minimizing delays and enhancing service. Initial signs indicate that specific types of delays, including those from braking problems and track defects, declined on the A line during the pilot. However, the MTA warns that more analysis is required to verify a direct connection to the program.
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.
Mientras Sarno reflexiona sobre el proyecto, destaca el potencial de las soluciones impulsadas por inteligencia artificial para transformar el transporte público. «Esta tecnología nos permite identificar problemas con anticipación, reaccionar más rápido y, en última instancia, ofrecer un mejor servicio a nuestros clientes,» afirmó.
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.
