Fair transit trip planning in emergency evacuations: A combinatorial approach
Bibliographic record
Abstract
This paper introduces the concept of proportional fair trip planning in the context of short-notice transit-based emergency evacuation. Proportional fairness attempts to meet social fairness among evacuees without sacrificing the efficiency of the evacuation process. The proportional fair trip planning concept is compared to the commonly used maximum safety concept that attempts to maximize the summation of safety functions of evacuees. We use a combinatorial approach to model the transit mass emergency evacuation in moving people from dangerous areas to safe shelters. High-density population and medium-density population variations of the problem are studied. For each variation of the problem, we study the computational complexity of the problem. We develop polynomial or pseudo-polynomial algorithms for each problem. Our numerical analysis shows that pure consideration of efficiency may result in highly unfair plans that only consider the portion of the population with the most payoffs (e.g., the population with the highest danger level) while ignoring the rest (potentially the vast majority of the population). While still considering efficiency, proportional fairness is shown to address this issue by also allocating resources to the population that has non-optimal payoffs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".