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Record W2107788297 · doi:10.3141/1762-07

Commuter Rail Station Governance and Parking Practices

2001· article· en· W2107788297 on OpenAlexaboutno aff
David Wilcock

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsTicketBusinessAgency (philosophy)Corporate governanceTrainControl (management)Transport engineeringPrivate sectorFinanceEngineeringEconomic growthEconomicsComputer security

Abstract

fetched live from OpenAlex

The station governance and parking practices of 19 commuter rail services in the United States and Canada are summarized. Unique or innovative approaches to address ownership and management issues are described. Unlike most urban transit systems, commuter rail authorities often do not control stations or parking. Wide-ranging practices exist, from outright facility ownership and control by the authority to no ownership or control. Many agencies and the private sector, including freight railroads with several services, are involved in ownership and management. Older, more established systems have the most complex ownership arrangements and operating practices. The authority, municipalities, and private sector of many systems are involved in the ownership and management of station buildings and parking facilities. Many authorities continue to provide ticket agents at stations, and a fee is typically charged for parking, which is often collected by the municipality. Passenger platforms are generally owned and maintained by the authority. Newer commuter rail services have less complex practices. Sometimes the authority is not involved with the station building or parking, although it typically owns and maintains the platforms. Many newer services also use technology more extensively, including ticket vending machines, to reduce the need for station personnel. Parking at the newer systems is generally provided free of charge, although various ownership and operating arrangements exist. Several systems have developed innovative approaches to address parking capacity issues. Partnerships with municipal governments, private companies, and community organizations to share parking are also used to reduce agency expenditures for new parking.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.119
GPT teacher head0.364
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2001
Admission routes1
Has abstractyes

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