Tackling subway delays: MTA and Google’s collaborative effort

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(MTA) has partnered with Google for a groundbreaking pilot project designed to enhance the dependability of its outdated subway network.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.

“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.

“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

Tackling delays with AI and smartphones

El programa TrackInspect se centra en abordar un aspecto crucial del problema: detectar y solucionar problemas mecánicos antes de que se agraven. Durante la prueba piloto, se instalaron seis teléfonos Google Pixel en cuatro vagones R46 del metro, reconocidos por sus asientos de color naranja y amarillo. Los dispositivos registraron 335 millones de lecturas de sensores, más de un millón de datos de GPS y 1,200 horas de audio.

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.

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.

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%.

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%.

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.

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 movement in transit advancements

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 ya ha colaborado anteriormente con otras agencias de transporte. El gigante tecnológico ha creado herramientas para optimizar la programación de Amtrak y se ha aliado con proveedores de tecnología de estacionamiento para integrar datos de aparcamiento en la calle en Google Maps. No obstante, la envergadura y complejidad del sistema de metro de Nueva York hace que este proyecto sea especialmente ambicioso.

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

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.

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.