Simulating Car-pedestrian Interactions during Mass Events with DTA Models: The Case of Vancouver Winter Olympic Games
Bibliographic record
Abstract
The objective of this paper is to present the application of a within-day Dynamic Traffic Assignment (DTA) Model to simulate ordinary, evacuation and emergency scenarios for downtown Vancouver during the forthcoming Winter Olympic Games. Within this context, the main problem was to simulate different kinds of pedestrians and vehicles-pedestrians interactions; these congestion phenomena can occur in presence of the unusual demand produced by important public events, such as sport games, music concerts and political rallies, when significant levels of pedestrian and vehicle flows are concentrated in space and time, i.e. converge to or diverge from one point/area in a relatively short time interval. Additionally, other phenomena that needed to be addressed where: - pedestrian route choice, since several routes are available to reach and leave event locations; - special event temporal demand, with time peaks concentrated around begin and end of events; - pedestrian capacity constraints, due to the limited capacity of road and sidewalks; - short term closure of streets, due to Olympic security measures. In order to address the above modeling needs, different approaches where considered, from classical static assignment, to meso simulation, to micro simulation applied to pedestrians and vehicles. Finally, a Macroscopic Dynamic Assignment model calculating Dynamic User Equilibrium was adopted, suitably extended in order to represent pedestrian flows and vehicle-pedestrian interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".