Network-wide mixed-rail traffic scheduler: challenges and implementation aspects
Bibliographic record
Abstract
In 2020, Indian Railways took up a de novo timetabling exercise for the Golden Quadrilateral and Diagonals (GQD) connecting Mumbai-Delhi-Kolkata-Chennai, involving about 1700 trains on a weekly basis over a network of 9,099 km. Based on significant participation in this activity, this paper elaborates on the challenges faced in a network-wide simulation and scheduling of trains across a large network. The methods to address these challenges and the software implementation aspects are also described. A network-wide mixed-rail traffic simulator was used successfully to construct a daily timetable for the GQD. The two primary goals of the Zero Based Timetabling (ZBTT) project were to generate train schedules that were: (a) feasible (in terms of kinematic constraints on both trains running and block-section, running lines) and (b) conflict-free (due to multiple trains having to share resources and the need to ensure time-durations of resource usage are different, subject to safety constraints), for a proposed set of departure timings and halt patterns of all the services that were planned on the GQD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".