Optimizing Mass Transit Utilization in Emergency Evacuation of Congested Urban Areas
Bibliographic record
Abstract
This paper presents how the capacity of mass transit can be optimally operated to alleviate congestion pressure during the evacuation of busy urban areas. The proposed model extends the traditional vehicle routing problem (VRP) to include: Multiple Depots to better distribute the transit fleet, Time Constraints to account for the evacuation time window, and 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 (MDTCPD-VRP). A framework, using Constraint Programming (CP), is developed to model and solve the MDTCPD-VRP evacuation problem. An Optimal Spatoi-Temporal Evacuation (OSTE) model is performed first to optimize the evacuation of the background vehicular traffic, generating transit travel cost (i.e. link travel times) as an input to the MDTCPD-VRP. We apply our methodology on a case study of a hypothetical evacuation event in the busiest core of the downtown area of the City of Toronto, Ontario, with 65% of total evacuees are transit-dependent. The results show the optimal scheduling and routing plan for transit-vehicles as the solution to the evacuation problem defined as MDTCPD-VRP. The equilibrium mode-split between traffic and transit (at which the total vehicle-travel time is equal for vehicular drivers and transit users) is found to be at 25% and 75%, respectively.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".