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
Summary. This paper develops a machine learning model to predict when transit vehicles need replacement in small urban and rural U.S. transit systems. Using historical data from retired vehicles, the model applies random forest and gradient boosting techniques to estimate service life, identify maintenance backlogs, and forecast replacement costs. The tool helps transit agencies maintain vehicles in good repair, reduce backlogs, and make better funding decisions for asset management.
Cite this article
Mistry, D., & Hough, J.. (2024). 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. Transportation Research Record Journal of the Transportation Research Board. https://doi.org/10.1177/03611981241235197
Mistry, Dilip, and Jill Hough. “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.” Transportation Research Record Journal of the Transportation Research Board, 2024. https://doi.org/10.1177/03611981241235197.
Mistry, Dilip, and Jill Hough. 2024. “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.” Transportation Research Record Journal of the Transportation Research Board. https://doi.org/10.1177/03611981241235197.
@article{mistry-2024-data-driven-approach-state-good,
title = {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},
author = {Dilip Mistry and Jill Hough},
journal = {Transportation Research Record Journal of the Transportation Research Board},
year = {2024},
doi = {10.1177/03611981241235197},
url = {https://doi.org/10.1177/03611981241235197}
}
TY - JOUR TI - 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 AU - Dilip Mistry AU - Jill Hough JO - Transportation Research Record Journal of the Transportation Research Board PY - 2024 DO - 10.1177/03611981241235197 UR - https://doi.org/10.1177/03611981241235197 ER -
Details
- DOI
- 10.1177/03611981241235197
- Countries
- United States
- Regions
- North America
- Categories
- transportation, rural-data-and-definitions
- Added
- 2026-04-28