Evacuation and Reentry Curves for Wildfire Evacuations Using Network Mobility Data
Bibliographic record
Abstract
With the rise in frequency and severity of wildfire events, understanding evacuation behavior is instrumental for emergency planning. Since evacuations have a strong temporal component, the departure and reentry times of people affect evacuation time estimates (ETEs) and congestion. To better understand emergent behavior and to analyze the temporal patterns in evacuations for improved evacuation planning, this paper investigates the movement of people out of hazard areas into host communities during the May 2023 Alberta Wildfires. While most data for these analyses are collected via traffic counts, mobile phones, or surveys, this research overcomes several limitations of these sources via privacy-protected network mobility data provided by the TELUS Data for Good program. From these data, unique devices were located during the evacuation and reentry time periods to model curves in four communities in Alberta ranging in population from 500 to 8,000: Drayton Valley, Edson, Fox Creek, and Rainbow Lake. The resulting departure time sequences first show generally fast evacuations that best match log-normal and log-logistic functions, and slower multiday, bimodal reentry curves. Second, ETEs were 6 to 10 hours and reentry estimates were 23 to 29 hours, which could be integrated into evacuation simulations and emergency planning. Third, the results suggest stable evacuation “S-shaped” curves and functions that could be used for general wildfire evacuation planning, particularly for smaller, auto-centric communities. Finally, the research showcases the benefit of deidentified network mobility data as an alternative data source and the increasing need to consider host communities in evacuations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".