Traffic Performance Indicators for Evacuation: The Case Study of the 2020 Silverado Wildfire
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study aims to facilitate the study of traffic dynamics in wildfire evacuation scenarios. To do so, it defines a set of traffic performance indicators to investigate traffic dynamics before and during wildfire evacuation events. These indicators include the following: system efficiency, travel time ratio, level of service, and jam time. To highlight the benefits and effectiveness of using these indicators, we demonstrated their application through the context of the 2020 Silverado fire in California, USA. In total, 66,924 traffic data points from 18 locations were obtained through the publicly available dataset of the California Department of Transportation. Results indicate a 7.5 km/h speed reduction during evacuation compared to routine conditions. In addition, traffic performance indicators confirmed that evacuation conditions may increase the times needed to reach destinations. This paper also demonstrates the need for using dedicated relationships for wildfire evacuation for developing, calibrating, and validating traffic modeling tools.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it