Predictive Maintenance Solution for Urban Rail Networks

This solutions brief outlines a predictive maintenance approach developed by Presidio in collaboration with Google Public Sector to enhance the safety and efficiency of urban rail networks. The document details the challenges faced by a major US city's transit authority, which operates 472 subway stations and over 6,500 subway cars, in maintaining their infrastructure using traditional inspection methods. Presidio's innovative solution involves retrofitting Google Pixel smartphones onto subway cars to collect critical data, including GPS location, accelerometer readings, and audio recordings. This data is transmitted to Google Cloud for analysis, where advanced AI and machine learning models identify track nonconformities in near real-time and predict future maintenance needs. The implementation of this system has led to significant improvements in safety, efficiency, and cost savings, as well as empowering maintenance workers with tools for informed decision-making. The brief emphasizes the transformative impact of modern technology on public transit infrastructure.