State of Good Repair Predictive Model for Small Urban and Rural Transit System’s Rolling Stock Assets

Achieving and maintaining public transportation rolling stocks in a state of good repair is very crucial to providing safe and reliable services to riders. Transit agencies that seek federal grants must also keep their transit assets in a state of good repair. Therefore, transit agencies in small urban and rural transit systems need an intelligent predictive model for analyzing their transportation rolling stocks, determining the current conditions, predicting when they need to be replaced or rehabilitated, and determining the funding needed to replace in a future year to maintain the state of good repair. Since many transit agencies in small urban and rural transit systems do not have adequate analytical tools for predicting the service life of vehicles, this simple predictive model would be a valuable resource for their state of good repair needs and their prioritization of capital needs for replacement and rehabilitation. The ability to accurately predict the service life of revenue vehicles is crucial in achieving the state of good repair. In this research, three unique tree-based ensemble learning methods have been applied to build three predictive models. The machine learning methods used in this research are random forest regression, gradient boosting regression, and decision tree regression. After evaluation and comparison of the performance results among all models, the gradient boosting regression model with the top 35 most important features was found to be the best fit for predicting the service life of transit vehicles. This model can be used to predict the projected retirement year for all small urban and rural vehicles in operation.

• Record URL:
  https://www.ugpti.org/resources/reports/details.php?id=1019
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/72867
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Upper Great Plains Transportation Institute

  Small Urban and Rural Center on Mobility
  North Dakota State University
  Fargo, ND  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Mistry, Dilip
  • Hough, Jill
• Publication Date: 2020-12

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; References; Tables;
• Pagination: 252p

Subject/Index Terms

• TRT Terms: Asset management; Forecasting; Machine learning; Maintenance management; Regression analysis; Rolling stock; Rural transit; Service life; Urban transit
• Subject Areas: Maintenance and Preservation; Planning and Forecasting; Public Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01764833
• Record Type: Publication
• Report/Paper Numbers: SURTCOM 20-03
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Feb 17 2021 10:44AM