How contraflow enhances clearance time during assisted mass evacuation – A case study exploring the Australian 2013–14 Gippsland bushfires
Bibliographic record
Abstract
Evacuation during a catastrophic disaster is a crucial operation that needs to be appropriately managed and is of more importance when considering the elderly and people with disabilities. The uncertain and unpredictable nature of disasters can cause long-term repercussions, especially in traffic congestion. This study presents a mathematical model to formulate traffic balance for regular and assisted evacuation (that is, disabled and the elderly) whilst considering traffic congestion in evacuation clearance time by applying contraflow. A Branch and Price (B&P) related approach is developed to help solve the proposed model in large-size problems. The presented algorithm is applied to a case study of Australia’s 2013–14 bushfires in Gippsland, located in the eastern part of Victoria. A variation test is performed to evaluate the robustness of results generated by the developed model. Results indicate that the participation percentage of edges is different based on their location, capacity, and sustainability of blockage. The edges’ capacity influences the evacuated population most compared to route capacity and time window. The output of this approach enables authorities to improve the resilience of communities by making optimal strategic and operational decisions for enhancing an evacuation response as well as influencing appropriate policies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".