Railway Rapid Transit Timetables with Variable and Elastic Demand
Bibliographic record
Abstract
This paper focuses on the design of railway timetables considering a variable elastic demand profile along a whole design day. Timetabling is the third stage in the classical hierarchical railway planning process. Most of previous works on this topic consider a uniform demand behavior for short planning intervals. In this paper, we propose a MINLP model for designing non-periodic timetables on a railway corridor where demand is dependent on waiting times. In the elastic demand case, long waiting times lead to a loss of passengers, who may select an alternative transportation mode. The mode choice is modeled using two alternative methods. The first one is based on a sigmoid function and can be used in case of absence of information for competitor modes. In the second one, the mode choice probability is obtained using a Logit model that explicitly considers the existence of a main alternative mode. With the purpose of obtaining optimal departure times, in both cases, a minimization of the loss of passengers is used as objective function. Finally, as illustration, the timetabling MINLP model with both mode choice methods is applied to a real case and computational results are shown.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| 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".