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© 2025 Upper Great Plains Transportation Institute is a research and education center at North Dakota State University.
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MPC |
| Title: | Updating the Uniform Rail Costing System Regressions |
| Principal Investigators: | John Bitzan |
| University: | North Dakota State University |
| Status: | Terminated |
| Year: | 2000 |
| Project #: | MPC-201 |
The Uniform Rail Costing System (URCS) is used to estimate individual railroad shipment variable costs for regulatory purposes. It is composed of a three phase process, as follows: (1) in Phase I, regression equations are estimated for 16 individual cost accounts, where output and capacity variables are used as independent variables; (2) in Phase II individual railroad unit costs are estimated by multiplying the percent of each cost account's expenses that are estimated to be variable by the railroad's total cost in that particular account and dividing by the number of service units (the percent of each cost account's expenses that are variable is estimated using the regression coefficients estimated in Phase I, along with individual railroad output and capacity measures); (3) in Phase III the number of service units (e.g. gross ton-miles) are computed from the attributes of the shipment, multiplied by each unit cost, and summed to get total variable cost. The accuracy of the entire URCS process depends on the accuracy of the Phase I regressions, as these are used to estimate the percent of various cost accounts that are variable. The regression coefficients use d to estimate cost variability in URCS reflect 1978-1985 data. Many mergers have occurred since then, and many changes have occurred in the locomotive fleet, traffic control, and other aspects of railroad operations.
Many smaller railroads—including some beltway railroads—were included in the 1978-1985 data set. None of the railroads in the 1978-1985 data set approaches the size of the BNSF, the UP, and the CSX and NS systems of today. Because of the concentration that has occurred since 1985, the Class I industry of today may exhibit different characteristics than the industry of the early 1980s. This study will re-estimate the Phase I URCS regressions using current data.
