Simulation of an Interdependent Metro-Bus Transit System to Analyze Bus Schedules from Passenger Perspective
Bibliographic record
Abstract
Simulation has been a very important tool in scheduling rapid transit systems (metro). We took an interdependent transit system comprising of metro and bus and looked at the existing bus schedule from passenger-experience perspective. Passenger experience of metro users is described by typical waiting time. For buses, user satisfaction of commuters depends mainly on waiting time in queues and length of queue. Also commuters get dissatisfied when they wait in queue and yet fail to take the bus and have to wait for the next bus. In this paper, at different metro stations, fixed schedule metro and bus arrival is simulated and field data of passenger arrival is added to that. Average waiting time for each bus is observed in this regard. A bus schedule is changed here and the effect of the change on the waiting time also is observed. Following this practice, feasibility studies for new bus schedule can be carried out to obtain certain levels of user satisfaction. Three stations from the Montreal Metro system are taken for this purpose. The system is simulated with simulation software Arena.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".