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Record W3081691901 · doi:10.1061/9780784483176.001

A Critical Review of Transit Level of Service Measures and an Overview of a Proposed New Approach

2020· review· en· W3081691901 on OpenAlexaff
Kaushan W. Devasurendra, S. C. Wirasinghe, Lina Kattan

Bibliographic record

VenueInternational Conference on Transportation and Development 2020 · 2020
Typereview
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMeasure (data warehouse)Computer scienceQuality of serviceService (business)Service levelTransport engineeringTransit (satellite)Quality (philosophy)Service qualityPublic transportLevel of serviceOperator (biology)Operations researchRisk analysis (engineering)TelecommunicationsEngineeringBusinessData miningMarketing

Abstract

fetched live from OpenAlex

Transportation industry suffers from the lack of an efficient, widely accepted, and widely applicable overall level of service (LOS) measure, specifically a one that can assess and compare the overall quality of service (QoS) of transit lines or systems or different operational performance of the same transit line or system. This study critically reviews major domains of transit level of service (TLOS) measures in both the industry and academic literature. It focuses on success in achieving anticipated goals as opposed to the requirement of such a measure. This study indicates that existing measures fall short in incorporating a combined view of both the passenger and operator and in assessing the overall TLOS by a single measure. The study further suggests a new approach to assess TLOS that has the potential to address these drawbacks.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0130.014
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.457
GPT teacher head0.447
Teacher spread0.010 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2020
Admission routes1
Has abstractyes

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