Data-Driven Approach to State of Good Repair: Predicting Rolling Stock Service Life with Machine Learning for State of Good Repair Backlog Reduction and Long-Range Replacement Cost Estimation in Small Urban and Rural Transit Systems

Abstract

This paper presents a data-driven approach to address the state of good repair (SGR) in small urban and rural transit systems in the U.S. by predicting the service life of rolling stock vehicles. Achieving and maintaining public transportation rolling stock in SGR is crucial to providing safe and reliable services to riders, particularly for transit agencies utilizing federal grants that mandate asset maintenance at a full level of performance. In this context, an intelligent predictive model is proposed to analyze the transportation rolling stock, determine their backlog and current condition, predict their replacement or rehabilitation needs, and estimate the funding required for future replacements, ensuring SGR. The model utilizes historical data from retired revenue vehicles in the National Transit Database and employs machine learning techniques, specifically random forest regression and gradient boosting regression, to develop the predictive tool. By addressing the backlog of vehicles beyond their service lives and estimating replacement costs, the model provides valuable decision support for effective transit asset management. Transit agencies in small urban and rural transit systems, lacking analytical tools for service life prediction, can greatly benefit from this simple yet powerful predictive model. The approach offers a practical resource to address SGR needs, optimize investment prioritization, and enhance operational efficiency in public transit systems.