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Record W2280134889 · doi:10.3141/2538-08

All-Door Boarding in San Francisco, California

2015· article· en· W2280134889 on OpenAlexaff
Jason Lee, David Papas

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2015
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsCanadian Wood Council
Fundersnot available
KeywordsTicketRevenueTransit (satellite)BoomPaymentTransport engineeringPublic transportBusinessPhoneService (business)PopulationAgency (philosophy)EngineeringFinanceMarketingComputer scienceComputer security

Abstract

fetched live from OpenAlex

For generations, the transit industry has had to balance the desire for faster boarding with the need to collect fares. On July 1, 2012, the San Francisco Municipal Transportation Agency (SFMTA) in California addressed this challenge by becoming North America's first multimodal transit operator to implement all-door boarding systemwide. Customers with valid fare media may enter through any door of any vehicle at any time. Unlike other transit providers that used proof of payment, SFMTA still allowed customers to pay cash on board vehicles at surface stops, thus avoiding the expenses associated with wayside ticket vending machines. San Francisco's operating environment provided ideal conditions to demonstrate the potential benefits of all-door boarding. Serving the nation's second-densest major city with crowded transit vehicles largely operating in mixed traffic, SFMTA must make efficient use of every minute in revenue service and cannot afford excessive time at stops. Two years after the policy's implementation, a comprehensive and multi-factor analysis revealed incremental improvements in dwell times and fare compliance. Overall bus speeds increased slightly despite ridership growth and a population and employment boom.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.117
GPT teacher head0.378
Teacher spread0.261 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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