Optimal Spatiotemporal Evacuation Demand Management: Methodology and Case Study in Toronto
Bibliographic record
Abstract
Emergency evacuation planning has drawn significant interest and attention over the past few years. The increasing rate of man-made disasters and natural catastrophes affecting major urban areas require comprehensive analysis and planning for emergency evacuation scenarios while harnessing the potential of Intelligent Transportation Systems (ITS) to expedite the evacuation process. Numerous studies, formulations, and control approaches have been presented in the literature with the common goal of improving the evacuation process to save precious time and lives. These studies are important contributions to the state of the art. However, the need still exists for integrating the various demand management and supply control strategies to synergize their potential benefits to emergency evacuation. The focus of this paper is to address the demand side of the problem and integrate demand scheduling and destination choice optimization. Towards integrating demand scheduling and destination choice, we attempt to dynamically route traffic during evacuation, capture the dynamics of both the loading and the evacuation profiles with time, utilize genetic algorithms as an optimization tool to fulfill the evacuation goal, and provide evacuees with optimal spatio-temporal guidance throughout the emergency evacuation process. The result is an optimal spatio-temporal evacuation (OSTE) model and tool that helps evacuees to decide where to go, when to go, and how to get there, i.e. the output of this model is the optimal departure times, destinations and paths for each evacuee. A case study applying the model to a portion of Downtown Toronto in a simulated environment is also presented.
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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) | 0.001 | 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".