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Record W2049362623 · doi:10.1002/atr.5670380205

The development and implementation of the operation system and data bank for the intelligent transportation system — sitcuo

2004· article· en· W2049362623 on OpenAlexaffvenue
Li Weigang, Yaeko Yamashita, Marlon Winston Koendjbiharie, Ricardo Cezar de Moura Jucá, A MacIver

Bibliographic record

VenueJournal of Advanced Transportation · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGlobal Positioning SystemComputer scienceRelational database management systemDatabaseThe InternetInformation systemJavaTransport engineeringRelational databaseWorld Wide WebTelecommunicationsOperating systemEngineering

Abstract

fetched live from OpenAlex

Abstract SITCUO (Sistemas de Informações de Transportes Coletivos Urbanos por Õnibus) ‐ a dynamic information system for urban bus passengers in Brasilia, using business intelligence, was developed to optimize bus operations and increase the satisfaction of urban transportation users. In order to achieve these objectives the system involves the convergence of a number of different technologies, including: Global Positioning System (GPS), Geographic Information System (GIS), database, data mining, Internet and telecommunications. The system includes communication between the GPS, the database, the Control Centre and the user interfaces, which provide estimated bus arrival times via the information display panels and the Internet. The information system at the Control Centre was implemented by applying Java, JavaServer Pages (JSP), and a Relational Database Management System (RDBMS) using an object‐oriented approach. The paper will present the general description of the system, the algorithms for estimating the arrival time of the bus at the bus stop, the implementation procedure adopted, the results of experiments undertaken on a bus route in Brasilia and the conclusions.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.324
Teacher spread0.299 · 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

Citations8
Published2004
Admission routes2
Has abstractyes

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