Get me to the track on time! - Traffic management for the 2015 PanAm/ParaPanAm Games in the Greater Toronto Area
Bibliographic record
Abstract
The 2015 PanAm/ParaPanAm Games are scheduled for July and August 2015 in the Greater Toronto Area. Due to the overall level of congestion on area highways and the fact that the Games venues are distributed across a wide area, with the Athlete’s Village being located on the Toronto Waterfront, travel time reliability for athletes and officials is a key issue. The Ministry of Transportation of Ontario was assigned the responsibility of planning and implementing a traffic management strategy to ensure that athletes and officials could be at their venues “on time†while minimizing the impact on the travelling public. To facilitate the development and evaluation of traffic management strategies, a large multi-level traffic simulation model was developed using AIMSUN. While the “macro†and “meso†levels of the model were used at various stages in the process, the principal tool for operational analysis was the “hybrid†level, featuring “micro†operation on key expressway corridors and “meso†operation on the remaining expressways and arterial roads. The model includes 345 kilometres of expressways, 135 interchanges, approximately 2,000 km of surface streets, and 920 signalized intersections. In addition to the evaluation of Transportation Systems Management (TSM) strategies, the simulation model was used to evaluate the potential role of Travel Demand Management (TDM) in mitigating the traffic impacts of the Games.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".