The development and implementation of the operation system and data bank for the intelligent transportation system — sitcuo
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".