A simplified approach to determine the optimum operating speed on high-speed railway lines
Notice bibliographique
Résumé
In this study, cost–speed relationship for high speed railways was examined and optimum economic speed was investigated. The Eskişehir–Ankara section of the İstanbul–Ankara railway line, which is still under construction, was taken as the sample. This section has been completed and test runs are being conducted. The new line is constructed parallel to the old railway line and the operation speeds and operation forms of the trains on this line are not yet finalized. It is not yet clear what the maximum operation speeds of the trains will be and whether the operation will be restricted to only high speed passenger trains or a combination of passenger and freight trains will be used. Therefore, cost changes associated with speed for both operations were examined in the study. The speed for the lowest cost was investigated for the benefit of the operating institute. The rail line is scheduled to begin operation in 2010, and as such demand estimation values for 2010 and unit cost values of Turkish State Railways (TCDD) were utilized. Only construction and operation costs were analyzed, societal costs were not included in the study. Several costs were formulated independent ofthe speed, whereas the majority of them were formulated and calculated based on speed. Finally, the contribution of each studied cost component, in the total cost, and variations in these costs and total cost for different speeds for both operation conditions were analyzed in this study. It was found that some cost components increased and some decreased as the speed increases. Total cost, which includes all the cost components studied, initially dropped off then rose up as the speed incrementally increases. Minimum cost occurred at 200 km/h in the case of operating with only passenger trains, while it occurred at the second speed level in combined operation (where passenger trains are at the speed of 200 km/h and freight trains are at the speed of 90 km/h).
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».