Development of a microsimulation-based mass evacuation model for persons needing mobility assistance
Bibliographic record
Abstract
This research proposes a framework for microsimulation modelling of traffic evacuation, considering persons needing mobility assistance (PMA). The study develops a hybrid approach to evaluate four designated evacuation routes under different network conditions. These routes are incorporated into a microsimulation model utilizing dynamic traffic assignment for regular vehicles and pre-defined assignment for emergency vehicles (EVs). The model executes three traffic conditions under two scenarios to evaluate the Average Evacuation Time (AET) for an EV exiting the Halifax peninsula. The first scenario, ‘Out of Danger Zone' (ODZ), determines AET to exit the peninsula, while the second, ‘To the Shelter Location’ (TSL), evaluates AET to reach designated shelters. The results show that routes 1 and 4 are the fastest under case 3 for both scenarios, while case 2 is the most realistic. Under case 2, route 2 is the fastest for ODZ, and route 1 for TSL. The suggested method supports policymakers in planning PMA evacuations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".