An integrated model for evacuation routing and traffic signal optimization with background demand uncertainty
Bibliographic record
Abstract
SUMMARY Emergency evacuation in congested urban networks can be influenced by uncertain background travel demands, but this issue has not been fully investigated. In this study, evacuation links are prepared for exclusive use by evacuees. Under this assumption, an integrated model is proposed for determining flows on emergency evacuation routes and traffic signals at intersections in the presence of uncertain background travel demands. The problem is formulated as a bi‐objective bi‐level programming model based on the concept of robust optimization. It is assumed that background travel demands belong to an ellipsoidal likelihood region whose parameters are determined by a singly constrained gravity model. With the aim of maximizing the background traffic impact degree in the lower‐level model with a logit‐based stochastic assignment constraint and background demands constraint (the aforementioned ellipsoidal likelihood region), background traffic corresponding to worst‐case demands is determined by the Lagrange multiplier method. In the upper‐level model, two objectives, minimizing both the total travel time of evacuation flows and performance index of the whole network flows, are constructed to determine optimal evacuation flows and traffic signals. The Non‐dominated Sorting Genetic Algorithm II is employed to determine the Pareto solutions of this optimization problem. An example using Sioux Falls networks illustrates the validity of the algorithm. A field case involving the Jianye network around the Nanjing Olympics Sports Center shows the applicability of this algorithm. Copyright © 2012 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".