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Record W4388405488 · doi:10.1109/iv60283.2023.00061

Visual Knowledge Discovery from Public Transit Performance Data

2023· article· en· W4388405488 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsPublic transportComputer scienceProcess (computing)Transit (satellite)Service (business)Component (thermodynamics)DestinationsService providerWork (physics)Mode (computer interface)Knowledge extractionTransport engineeringData scienceBusinessData miningEngineeringHuman–computer interactionMarketingGeography

Abstract

fetched live from OpenAlex

Public transit is an important component of the day-to-day activities of many people. It provides a cost-effective and convenient way for individuals to commute to work, school, and other destinations. Public transit bus is a vital mode of transportation for students, as it enables them to commute to and from their educational institutions. Delays in bus schedules can have severe consequences, such as missing exams, meetings, and other important engagements. Hence, in this paper, we present a visual knowledge discovery solution to mine public transit bus on-time performance data and visualize the mined results. In particular, visual representation (e.g., graphs, time plots) from our visual knowledge discovery process help reveal factors contributing to bus delays in different neighborhood areas. This helps the service providers to improve their services, and thus enhance rider experience. Evaluation on real-life data from a Canadian city shows the practicality of our solution.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.102
GPT teacher head0.346
Teacher spread0.244 · 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

Quick stats

Citations6
Published2023
Admission routes3
Has abstractyes

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