A Study on Fitting Longitudinal Profiles of Railway Station Tracks
Bibliographic record
Abstract
The accurate fitting of railway station track profiles is crucial for improving the quality of station‐related engineering projects. However, in practice, there are still cases where straight lines are used to approximate vertical curve segments. To avoid the ‘straight‐line approximation’ deviation in the gradient value of the profile’s straight segments, this paper establishes a mathematical programming model with the objective of minimising the total amount of track raising and lowering. To satisfy the model constraints, an optimal vertical curve fitting algorithm, utilising a bisector vector approach to unambiguously resolve the conjugacy issue in analytical circular curve calculations, and a genetic algorithm based on neighbourhood search, specifically designed to fine‐tune initial least squares gradient estimates and avoid systematic deviations, are proposed. Finally, the effectiveness of the proposed model and algorithm is validated through a case study. With a total grade value adjustment of only 0.45‰ based on the least squares method, the proposed fitting algorithm achieves a 38.7% reduction in track raising and lowering compared to manual fitting. This demonstrates the effectiveness of the proposed method in achieving the integrated optimisation of straight‐line segments and vertical curve segments within the longitudinal profile. The implementation of this algorithm through programming can significantly enhance both the efficiency and quality of railway station track profile fitting.
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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".