Pricing Commuter, Intercity, and Freight Trains in a Terminal Railway Context: An Approach
Bibliographic record
Abstract
The cost-of-service (or fully allocated cost) pricing model has been criticized in the economics literature. The criticisms generally focus on the issues of cross-subsidization problems from using average costs and economically inefficient pricing. A fully allocated cost model, applied to a terminal railroad setting, is presented that can substantively overcome these criticisms by using a resource consumption approach for key cost drivers. A successful implementation of a new cost recovery system in Toronto, Ontario, Canada, is used that applies these resource consumption concepts. A necessary precondition to this approach is the charting of the classes of traffic (commuter, intercity, and freight) into operated-track segmented paths, each of which consists of a set of one or more track links. Each traffic class path is characterized as either consisting of sole-use links, joint-use links, or a combination thereof. Operating and capital costs directly attributable to the track links are calculated. A reverse engineering work-effort-per-activity approach is used for assigning the total routine maintenance of way and maintenance of signals budget dollars to the terminal track links. The resource consumption approach provides a logical framework and analytical platform for analyzing link infrastructure complexity; system, path, and link capacity; path and link cost performance; and path and link renewal and replacement capital planning and capital sharing responsibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".