Using Simulation to Analyze Crowd Congestion and Mitigation at Canadian Subway Interchanges
Bibliographic record
Abstract
With year after year of record ridership and demand only expected to grow, transit infrastructure is under increasing pressure. Examining the impact of the scheduling and coordination of subway lines at interchange stations is critical to reduce crowd congestion at station facilities. There is, however, a gap in knowledge concerning how crowd congestion is affected by the arrival patterns of trains. The effects of arrival patterns are especially critical at interchange stations where several train lines converge. A simulation-based analysis was performed to fill this knowledge gap. Field data were collected at the Bloor-Yonge Toronto Transit Commission subway station in Toronto, Ontario, Canada, a station known to be operating at capacity during peak periods. For performance of the analysis, a model of the station was developed, calibrated, and validated in the pedestrian simulator MassMotion. The congestion duration that passengers experienced was examined by varying the passenger volume and the arrival pattern of the two independent train lines. Adjusting the train arrival pattern was found to cause as much as a 63% reduction in the congestion passengers experienced. Additional scenarios were proposed as improvements over the status quo and tested for their significance in regard to improvement in congestion time experienced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".