How Do Individual Walk Lengths and Speeds, Together with Alighting Flow, Determine the Platform Egress Times of Train Users?
Bibliographic record
Abstract
Egress times of railway passengers from train alighting up to station exit typically amount to some tens of seconds, but with much variability even at the train level. Here, we first model the egress time as the ratio of the walk length to the preferred walk speed, under free-flow conditions. Then, we model the possible occurrence of congestion among the users alighting from a train as a traffic bottleneck affecting those passing at a “queue focal point” during a “queued time interval.” Analytical formulas are provided for the CDF and PDF of egress times, covering the free-flow case and the congested case. Their computation is straightforward for bivariate Gaussian length-speed walk pair. A maximum-likelihood method is developed, together with a quick estimation procedure. A case study of four contrasted trains serving an urban mass transit station in Paris is reported. One train experienced free-flow alighting conditions, whereas each of the other three had its own bottleneck. The MLE method enabled us to recover all parameters but one, due to an issue of identifiability: the solution was to take the mean walk speed as exogenous.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".