A simplified approach to determine the optimum operating speed on high-speed railway lines
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".