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Research Project
Develop a Revenue Vehicle Rehabilitation or Replacement Predictive Model for Small Urban and Rural Transit Systems

To maintain an efficient public transportation system, existing transit assets need to be kept in proper condition. Maintaining the condition of small urban and rural transit systems in a state of good repair has become a problem of national importance, and transit agencies have made achieving a state of good repair a high priority. Major limitations include underinvestment and lack of a good analytical tool for investment decisions. The main objective of this research is to build a machine learning predictive model to predict the projected replacement year of transit vehicles for rural and small urban transit agencies for the purpose of obtaining a state of good repair. A second objective is to build a financial analysis tool to estimate current backlogs and predict yearly projected vehicle replacement costs.

The machine learning predictive tool and financial analytical tool will help rural and small urban transit agencies to facilitate their state of good repair analysis and guide decision makers to make decisions on investing in rehabilitation and replacement. The detailed reports produced by these tools will be helpful for decision makers to prioritize investment needs for rehabilitation and replacement. This also includes elimination of investment backlog, replacement of transit assets reaching the end of their useful life, overall condition of their remaining service life, and projected yearly replacement costs. This tool will potentially tailor the replacement decision to a given system rather than solely rely on FTA's useful life policies or industry-wide experiences. Better pinpointing the boundary between "rehab and replace" will potentially allow better-informed capital decisions and, perhaps, better modulate capital-funding needs with available funding. Finally, this research will also offer more intuitive, softer criteria that managers and other stakeholders can use in formulating capital plans.

NDSU Dept 2880P.O. Box 6050Fargo, ND 58108-6050