Time-Based Model of Passenger Dispersion on Subway Platforms
Bibliographic record
Abstract
The platform is a key interface in the subway network, permitting transition of passengers between the trains and the station. Proper modelling of platform behaviour is critical to predict the dwell times that trains will experience. Research, however, has been limited to only a basic examination of the factors affecting the passenger dispersion along the platform. To fill this gap, a modelling framework was developed to produce a time-step evolution of passenger dispersion on platforms, inspired by the concept of diffusion and taking into account waiting section preference based on the locations of exits at destination stations. Models were estimated via a simulation-based Genetic Algorithm using data collected at several subway stations in Toronto during peak and off-peak periods. Validation was performed by examining overall deviation between the model results and collected data in predicting distribution of passengers on the platform at train arrival. Moderate performance was found on average, with progressively better results for higher volumes. This included, on average, a -6% difference in predicting the volume in the densest section, the limiting factor for train dwell time. Overall, the proposed model provides a more comprehensive modelling framework than has previously been attempted, towards improving platform design and predicting the effects of crowd management on line operation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".