The Impact of Off-peak Delivery on Urban Freight Movements during the Pan American Games
Bibliographic record
Abstract
Large scale sporting events provide distinct challenges to urban freight movement. The 2015 Toronto Pan and Parapan American Games introduces high demand for goods and services as well as delivery restrictions in key sections of Toronto. First-hand accounts from members of London, England’s freight community and relevant literature are presented as a case study on best practices, including off-peak delivery, for freight delivery during such sporting events. A second case study of Nestlé Canada examines the benefits of advanced routing and off-peak delivery in mitigating the impact of the Games, as well as the potential for reducing the fleet size. A heuristic model is used to identify and select off-peak customers and to estimates route travel times. The results show that the Games are expected to increase Nestlé travel times by 6.4%, and that off-peak delivery can be used to reduce the travel time impacts by an average 2.9%.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".