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Record W2612488123 · doi:10.1139/cjce-2017-0294

Breaking into emergency shuttle service: Aspects and impacts of retracting buses from existing scheduled bus services

2018· article· en· W2612488123 on OpenAlexafffundvenueabout
Ehab Diab, Guangnan Feng, Amer Shalaby

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSoftware deploymentService (business)Public transportTransport engineeringService qualityBridging (networking)Bus rapid transitLevel of serviceQuality of serviceComputer scienceEngineeringBusinessTelecommunicationsComputer securityMarketing

Abstract

fetched live from OpenAlex

High-quality transit service is a vital aspect of any modern city. When unexpected interruptions to the transit service occur, they reduce the quality of service provided to the public. One of the main strategies that is employed to deal with rail service interruptions is “bus bridging,” whereby buses from scheduled services are deployed to offer shuttle services. Very few efforts are found in the literature that have investigated this policy’s effectiveness. Therefore, this study aims at exploring the different aspects and impacts of retracting buses from scheduled services in response to subway and streetcar service interruptions in Toronto. It explores the size of the deployment, as well as the system response and recovery times using detailed subway and streetcar shuttle service reports collected in 2015. The paper shows remarkable fluctuations not only in the utilized number of shuttle service buses over time, but also on the service response and recovery times.

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)0.000
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 score0.722
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.259
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

Citations13
Published2018
Admission routes4
Has abstractyes

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