Large-Scale Evacuation Using Subway and Bus Transit: Approach and Application in City of Toronto
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
Public transportation systems play a significant role in emergency evacuation. Therefore, this paper is geared towards harnessing subway and bus transit to alleviate congestion pressure during evacuation of busy urban areas. Routing and scheduling of transit vehicles and subway operation is envisioned as a new variant of the well-established vehicle routing problem. The model presented in this paper combines multiple variants of the traditional vehicle routing problem while reflecting on the operational characteristics during emergency evacuation, to include (1) multiple depots to better distribute the transit fleet, (2) time constraints to account for the evacuation time window, and (3) constraints for pick-up and delivery locations of evacuees. The evacuation problem is hereafter defined as a multi-depot time-constrained pick-up delivery vehicle route problem. A framework, using constraint programming and local search methods, was developed to model and solve the problem. An optimal spatio-temporal evacuation model was performed first to optimize evacuation of background vehicular traffic, generating transit travel cost (i.e., link travel times) as an input to the evacuation problem. The methodology was applied to evacuate the entire city of Toronto. The results show that the Toronto Transit Commission fleet is capable of evacuating the transit-dependent population (1.34 million) within 2 h on average. The four subway lines of the city of Toronto carry approximately 0.62 million people and can evacuate these people in less than 3 h on average. Toronto Transit Commission shuttle buses (1,320 vehicles) can evacuate the remainder of the transit-dependent population (0.72 million) in approximately 1.5 h on average.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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