Avaliação dos principais métodos analíticos de cálculo de capacidade de tráfego utilizados em ferrovia nacional e internacional
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».