Multiobjective Optimization for Multimodal Evacuation
Bibliographic record
Abstract
This paper proposes a multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. The multi-objective approach optimizes the multimodal evacuation framework by investigating three objectives: minimizing in-vehicle travel time, minimizing at-origin waiting time, and minimizing fleet cost in the case of mass transit evacuation. For auto evacuees, an optimal spatiotemporal evacuation (OSTE) formulation is presented for generating optimal demand scheduling, destination choice, and route choice simultaneously. OSTE implements dynamic traffic assignment techniques coupled with genetic optimization to achieve the objective functions. For transit vehicles, a multiple-depot, time-constrained, pickup and delivery vehicle routing problem (MDTCPD-VRP) is formulated to model the use of public transit shuttle buses during evacuation. MDTCPD-VRP implements constraint programming and local search techniques to achieve the objective function and satisfy constraints. The OSTE and MDTCPD-VRP platforms are integrated in one framework to replicate the impact of congestion caused by traffic on transit vehicle travel times. This paper presents a prototype implementation of the conceptual framework for a hypothetical medium-size network in downtown Toronto, Ontario, Canada. The results show that including the waiting time and the in-vehicle travel time in the objective function reduced the network clearance time for auto-evacuees by 40% compared with including only the in-vehicle travel time. For mass transit, when considering fleet cost, an increase of 13% in network clearance time for transit evacuees was observed with a decrease of 12% in fleet size. Mass transit was shown to provide latent transportation capacity that is needed in evacuation situations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".