Driving behaviour during wildfire evacuation
Bibliographic record
Abstract
Introduction: Wildfires pose escalating risks to communities at the wildland--urban interface, often necessitating extensive evacuations. This thesis explores how driving behaviour during wildfire evacuations differs from routine conditions and how these differences can be modelled to improve traffic simulations, thereby supporting more informed planning and response decisions by authorities.Objectives: Three main research objectives are addressed: (1) characterising how macroscopic traffic dynamics differ between wildfire evacuations and routine traffic conditions, (2) assessing the impact of wildfire smoke on car-following behaviour, and (3) developing a framework for traffic simulations that can support emergency planning for and response during wildfire evacuations.Methods and outcomes: (1) A dedicated data analysis method was developed to compare traffic dynamics during routine and evacuation scenarios, using traffic detector data from recent wildfire events in California. The analysis revealed that drivers move more slowly and leave larger gaps between vehicles during evacuations. If unaccounted for in simulations, these behavioural shifts can lead to overly optimistic evacuation time estimates.(2) A custom driving simulator and virtual reality environment were designed to assess how reduced visibility from wildfire smoke affects driver behaviour. Results indicate that, under reduced visibility, drivers reduce their speed when travelling alone, but do not consistently adjust their following distance in congested traffic. These findings inform a visibility-sensitive car-following model.(3) Finally, a simulation framework was proposed, combining data from real wildfire events, virtual reality experiments and evacuation drills. This framework supports the evaluation of evacuation strategies and planning decisions under varying conditions. A case study applied to a community of more than a thousand households in Colorado demonstrated the framework’s utility in assessing traffic management interventions.Conclusion: By capturing evacuation-specific driving behaviours and their impact on traffic, this thesis provides practical approaches to enhance the realism of evacuation models, which can, in turn, support more reliable planning and safer wildfire evacuations. ----------------------------------------------------------- Sponsoring organisations: Natural Resources Canada (NRC)National Institute of Standards and Technology (NIST) ISBN: 978-91-8104-699-1 (print)978-91-8104-700-4 (pdf) ISRN: LUTVDG/TVBB–1076–SEISSN: 1402-3504
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".