Avaliação dos principais métodos analíticos de cálculo de capacidade de tráfego utilizados em ferrovia nacional e internacional
Bibliographic record
Abstract
Railroad transport is becoming an ever more highly viable option for both freight and passengers transportation. That is a world-wide reality, and in the past few years the Brazilian railroad system has been receiving strong investments. Any investment in transportation has an expressive contribution in the economic and social development of the region that receives it; specially railroads, due to its large scale at low costs for its users. On the other hand, railroad represents a high cost of implementation, demanding refined criteria for defining its characteristics. Such definition inevitably has to consider the traffic capacity calculation as a premise, which is the amount of trains that can run on its tracks for a defined period of time, under pre-established control and safety conditions. To identify the traffic capacity of a railroad mesh constitutes a great challenge due to the high complexity and great number of elements that correlates to each other during traffic flow of trains. Traffic capacity calculations demand great precision, considering that its result is the base for economic viability both for a new railroad as for restructuration of an old one. Furthermore, with the new regulatory mark of Brazilian railroads, the surplus of railroad companies traffic capacity will be auctioned to the market through ANTT which is going to determine whether the current calculated and informed capacity of the railroads are in fact real. Thus, more than ever the precise calculation of mesh capacity is of extreme importance to the efficiency and efficacy management of railroads. The present study analyses qualitatively the main analytical methods available for obtaining mesh capacity that are broadly utilized in countries where the railroad transportation has a relevant share in the transportation matrix, plus Brazil; they are: USA, Japan, Germany, UIC - International Union of Railways: England, Spain, Italy, Russia and Canada. Beyond that, through the use of evaluation criteria based on categorization of railroads parameters, it was identified the most suitable calculation method. The results demonstrate a common fragility: unimportance being given to unplanned events; which should not happen, since they can significantly modify the calculation results. Five out of ten methods studied at least consider such parameter, and from the other half, only two methods indicate its insertion as a correction factor. Both strong and weak points of each of the ten methods assessed were identified, allowing an opportunity of directing efforts for their improvement. By the exposed above, the present research presented a new theme, innovative and very relevant. It is recommended that a more sustainable and technically criterious definition is developed for treating the parameter of unplanned or undesirable events, and a new method that encloses the highest number of best practices contained on the ten methods analyzed in this dissertation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".