Managing Large-Scale Multimodal Emergency Evacuations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents the development of a novel framework that optimizes the evacuation of large cities using multiple modes including vehicular traffic, rapid transit, and mass-transit shuttle buses. A large-scale evacuation model is developed for the evacuation of the City of Toronto in case of emergency. A demand estimation model is first designed to accurately quantify the evacuation demand by mode (drivers vs. transit users), over time of the day when the crisis begins, and over space (location). The output of the demand estimation model is then fed into two optimization platforms: (1) an optimal spatio-temporal evacuation (OSTE) model that synergizes evacuation scheduling, route choice, and destination choice for vehicular traffic and (2) a model based on a new variant of the vehicle routing problem to optimize the routing and scheduling of mass-transit vehicles. The study concluded that OSTE can clear the City of Toronto 4 times faster than the do-nothing strategy. The OSTE average automobile evacuation time for the 1.21 million people using their cars is close to 2 h. The optimization of the routing and scheduling of the readily available Toronto Transit Commission fleet (4 Rapid Transit lines and 1320 transit buses used as shuttles) can efficiently evacuate the transit-dependent population (1.34 million) within 2 h.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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 it