Impact of demand management policies on freight efficient land-uses in the Greater Toronto and Hamilton Area: A case study in the retail sector
Bibliographic record
Abstract
The efficiency of urban supply chains relies heavily on the location of logistics facilities in metropolitan areas , the spatial location of suppliers and receivers of goods, and the traffic conditions throughout the day. This research investigates whether optimal locations for Distribution Centers (DCs) are sensitive to changes in demand management policies (e.g., off-hour deliveries). Solving this research question is relevant to urban areas, considering how land-use policy changes (e.g., zoning) might take several years to achieve benefits. On the contrary, demand management policies produce an almost instantaneous impact. This research uses a bi-level mathematical optimization model that minimizes the social cost (summation of private costs plus externalities) and considers the effects of land-use decisions on the delivery tour patterns emanating from DCs. This research presents two case studies that identify the best locations for logistical facilities of different sizes in the Greater Toronto and Hamilton Area. The results show that making deliveries overnight results in fewer vehicles, more stops per tour, shorter travel times , smaller costs on a per-kilometer basis, and lower total emissions. The results also show that DC's optimal locations are near the main cargo attractors and have good access to highways and arterial roads. This research underscores the importance of freight-efficient land-use policies and demand management strategies that could help transportation and land-use professionals generate evidence-based policies for greener and more sustainable metropolitan areas.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".