GIS-Based Integrated Rural and Small Urban Transit Asset Management System

Project Details







U.S. Department of Transportation

Principal Investigator
Carl Kurt
Co-Principal Investigator
Pat Weaver

About the research

This study developed a methodology for improving the practice of making transit asset investment decisions at state departments of transportation (DOTs) and local transit agencies. The study made four major contributions to the state of transit asset investment decisions.

First, the report provides a review of the literature on the relationship between maintenance (preventive and corrective) costs and transit vehicle conditions, and it discusses its relevance to the vehicle conditions encountered in US transit agencies. The maintenance costs include both vehicle operating costs (i.e., fuel consumption, oil consumption, repair/maintenance, and depreciation) and non-vehicle operating costs (i.e., vehicle downtime due to maintenance work and road calls due to vehicle breakdowns on the road).

The majority of studies in this area find that there are significant differences in vehicle operating costs between road types (i.e., bituminous versus gravel versus earth), age, mileage, and vehicle type. Vehicle repair/maintenance costs are found to be primarily affected by vehicle condition. In terms of non-vehicle operating costs, vehicle downtime due to maintenance work and road calls due to vehicle breakdowns on the road were extensively studied in relation to vehicle condition.

The second contribution of the study is the development of a new vehicle deterioration model based on the ordered probit method. The major capability of the model is to predict the future conditions of the vehicle based on the historical records of the selected dependent factors, such as the vehicle?s age, mileage, current conditions, and so forth. To best predict the vehicle?s future condition, the most valuable dependent variables were identified.

The contribution of possible variables was analyzed and the factors that affect a vehicle?s future condition were specified. The model can identify the relative importance of the independent variables with the given condition ratings. In addition, predictions can be made for individual vehicles or a group of vehicles at different condition ratings, both of which are important for the management system. Knowing the percentages of vehicles at different condition ratings in the future based on the present and historical conditions, a transit fleet manager can allocate the budget more efficiently and accurately.

The third contribution of this study is the development of relationships between vehicle conditions and the cost of preventive and corrective maintenance and a life cycle cost analysis (LCCA) methodology incorporating these cost relationships into network level and project level decisions. One can use these relationships and LCCA to select the best maintenance strategies for short- and long-term operation.

The fourth contribution of this study is to develop an integrated transit asset management system to incorporate the developed models described above and help managers make decisions about which applicable maintenance to use on the basis of minimizing total cost.

Funding Sources:
U.S. Department of Transportation