Pre-disaster evacuation transport network design under uncertain demand and connectivity reliability: a novel bi-level programming model
Bibliographic record
Abstract
Evacuation transport network design plays a critical role in the efficiency of emergency response. This research proposes a novel bi-level nonlinear programming model for the pre-flood evacuation transport network design. The model considers both uncertainties of demand and network connectivity reliability. An upper-level model is developed with the minimum total evacuation time and maximum network connectivity reliability, while the lower-level model is a traffic assignment model that describes people’s evacuation route choice behavior. For the uncertain network connectivity reliability, an approach to quantify it based on percolation theory is proposed. For the uncertain demand, an approach to transform it into a solvable form based on Robust Optimization (RO) is proposed. Furthermore, the lower-level model introduces the regret-risk utility function as its objective function and proves its applicability. An equilibrium condition is proposed to improve the Logit model based on the regret-risk utility function. For the solution of this model, an Improved Genetic Algorithm combined with Non-dominated Sorting Genetic Algorithm II(IGA-NSGA-II) is designed. Then, the Nguyen-Dupuis network is used to demonstrate that the approach developed in this paper can be used to solve the bi-level nonlinear programming model and to obtain a satisfactory design solution. Further, a parameter sensitivity analysis is shown to study the impact of the risk aversion parameter and regret aversion parameter in the regret-risk utility function. Finally, the Central Coast region of New South Wales, Australia is used as a case study, and the research output will help government authorities to plan and design a pre-flood evacuation transport network, especially to answer the questions of “Where to build potential roads?”, “How much budget is needed?”, “How long does it take to evacuate?”, and “How reliable is the network connectivity?”.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".