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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".