Use of Agent-Based Crowd Simulation to Investigate the Performance of Large-Scale Intermodal Facilities: Case Study of Union Station in Toronto, Ontario, Canada
Bibliographic record
Abstract
When planning complex transit terminals, hubs, and stations, it is critical to analyze a facility’s capacity to handle expected passenger movements and volumes. In Toronto, Ontario, Canada, the revitalization of Union Station, the country’s busiest transit facility, involved the development of a set of high-fidelity pedestrian microsimulation models that were used to plan the improvement of this major intermodal hub. The pedestrian models were first created with MASSMOTION software, and construction plans and projected transit schedules were applied to represent various scenarios during and after the station revitalization. These models were then calibrated and validated against data from passenger counts and transit usage surveys to provide an accurate base for future scenarios. Through the use of a fully constructed model of the station with 2031 transit demand projections, a series of stress experiments was performed to evaluate the station’s capability to handle future passenger volumes. Under projected 2031 circumstances, it was found that Union Station could operate reasonably well. However, an increase in inbound transit passenger volumes of a mere 10% over 2031 projections could lead to severe levels of congestion within constrained sections of the station. This paper reports on the development of the microsimulation models and the model results for future scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".