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Record W1587664141 · doi:10.1177/0361198105192700129

Assessing Transit Level of Service along Travel Corridors

2005· article· en· W1587664141 on OpenAlexaff
Yaping Xin, Liping Fu, Frank Saccomanno

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of WaterlooStantec (Canada)
Fundersnot available
KeywordsTransit (satellite)Transport engineeringService (business)Service qualityLevel of servicePublic transportBus rapid transitQuality (philosophy)Travel timeService levelComputer scienceBusinessEngineeringMarketing

Abstract

fetched live from OpenAlex

This paper describes a case study of applying the recent edition of the Transit Capacity and Quality of Service Manual (TCQSM) to evaluate the quality of transit service on several travel corridors in an urbanized area. The study focuses mainly on four level-of-service (LOS) measures: service frequency, hours of service, service coverage, and transit–auto travel time. Assumptions are introduced to extend these measures, which are intended for a particular element of a transit system–-such as stops and route segments–-so that they become applicable for measuring the quality of transit service of travel corridors. An extensive case analysis indicates that all LOS measures are sensitive to various transit planning variables and can be easily calculated with readily available data. The research has also identified a range of issues with the current TCQSM methodology.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.268
GPT teacher head0.454
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2005
Admission routes1
Has abstractyes

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