The impact of wildfire smoke on traffic evacuation dynamics
Bibliographic record
Abstract
• Driving behaviour in wildfire smoke is studied in virtual reality. • Free-flow speeds decrease along with visibility due to smoke. • Distance headways are similar in all scenarios, regardless of the visibility. • A model of traffic evacuation dynamics in wildfire smoke is provided. This study investigates how reduced visibility due to wildfire smoke affects driving behaviour, specifically speed and headway, and the resulting implications for evacuation management and planning. Data were collected from participants immersed in a virtual environment through a driving simulator with a head-mounted display. Thirty-seven participants drove through scenarios simulating a rural highway. While driving visibility was systematically varied with virtual wildfire smoke. Participants were initially alone on the road to measure free-flow speeds and then proceeded to drive behind a convoy of cars. When visibility was low, driving speed was significantly reduced compared to the scenario with unrestricted visibility. Surprisingly, however, participants maintained similar distance headways in denser smoke compared to conditions with unrestricted visibility, suggesting that car-following behaviour was not affected. The collected data were used to develop a model that captures drivers’ responses to reduced visibility due to smoke. The proposed model can be integrated into both macroscopic and microscopic traffic models, providing a tool for estimating evacuation times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".