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Record W649628654

Economies of Scale in Operating Costs for LRT and Streetcars

2014· article· en· W649628654 on OpenAlexaboutno aff
Duncan W. Allen

Bibliographic record

VenueTransportation Research Board 93rd Annual MeetingTransportation Research Board · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEconomies of scaleTransit (satellite)MileTransport engineeringUnit (ring theory)Service (business)Public transportScale (ratio)Light rail transitBusinessModalModal shiftLight railKilometerEngineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

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 (light rail transit) 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 PTD (passenger traffic density), system size (route-miles), and ACS (average commercial speed). This paper explores these relationships based on data reported to FTA (Federal Transit Administration) and CUTA (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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.394
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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