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Record W587782956

GETTING IT RIGHT IN LIGHT RAIL: ANALYSIS OF UK PERFORMANCE

2004· article· en· W587782956 on OpenAlexaboutno aff
Ed Humphreys

Bibliographic record

VenueRail Transit ConferenceAmerican Public Transportation Association · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueLight railSubsidyLight rail transitProductivityWork (physics)Transport engineeringPublic transportOperations researchComputer scienceEngineeringTelecommunicationsBusinessEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

The UK Department for Transport (DfT) has powers to provide grant finance for part funding of light rail schemes. All new systems have required grant which has been a principal tool of urban transit policy. Other criteria must also be satisfied such as the need to avoid operating subsidy. To get a better understanding of why differences arise between schemes, the DfT commissioned a review of the comparative performance of light rail proposals to explore the wide range in costs, revenues and impacts. The results of that work inform the guidance on local public transport scheme appraisal and yield insights into what makes a successful scheme. The paper covers the appraisal and grant rules and the results of the DfT's comparative work. The UK now has 234 km (145 miles) of light rail route, mostly built since the pioneering Tyne and Wear scheme that opened in 1980. Comparison of the new UK systems shows a wide variation in performance covering revenue density, operating costs, ridership growth, load factors and productivity. Some of this reflects the different technology in use and some reflect different circumstances. Given recent cost increases, there is considerable uncertainty in British policy on further light rail investment. London's Docklands Light Railway (DLR) produces a Market Plan which benchmarks DLR performance against other UK systems and internationally, including selected light rail operations in USA, Canada and Hong Kong. These results, together with the DfT research, enable conclusions to be drawn about the key factors in the success of light rail schemes and the expected range of key performance indicators.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.204
Teacher spread0.191 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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