Economies of Scale in Operating Costs for Light Rail Transit and Streetcars
Bibliographic record
Abstract
Operating and maintenance (O&M ) costs receive less attention than might be warranted, given that they recur each year as part of a transit agency’s budgeting process. A number of things can be learned from the annual O&M costs incurred by the existing streetcar and light rail transit (LRT) systems operating in North America. First and foremost among these is that modal average ‘unit costs’ for O&M can be very misleading. The range in O&M costs per passenger-mile (the most objective overall measure of the cost of providing transportation service per unit of service actually consumed) varies by almost two orders of magnitude (from about 12 cents to almost 6 dollars), and substantial variances exist within individual modes due to the factors mentioned above. For LRT and streetcars, there are some significant economies of scale that drive down the O&M unit costs (per passenger-mile) between very small and very large systems. These can be better understood in terms of passenger traffic density (PTD), system extent (network route-miles), and average commercial speed (ACS). This paper explores these relationships based on data reported to the Federal Transit Administration and Canadian Urban Transit Association for the calendar year 2009, and identifies circumstances under which caution should be exercised in making generalizations about rail O&M costs.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".