Power Trips: Early Understanding of Preparedness and Travel Behavior During California Public Safety Power Shutoff Events
Bibliographic record
Abstract
Recent wildfire risks in California have prompted the implementation of public safety power shutoff (PSPS) events, procedures enacted by utility operators to deenergize parts of the electrical grid and reduce the likelihood of wildfire ignition. Despite their yearly occurrence, PSPS events are severely understudied, and little is known about how these events affect disaster preparation activity, travel behavior, and transportation systems. With growing wildfire risks in North America and beyond, PSPS events require immediate and thorough research to reduce their negative externalities and maximize their benefits. This exploratory study employs survey data from East Bay Hills residents in Alameda and Contra Costa counties in California who were affected by two PSPS events in October 2019 ( n = 210). Through descriptive statistics and basic discrete choice models for the decision to conduct typical or changed travel, this research contributes to the literature as the first assessment of PSPS event travel behavior. We found that travel did not change drastically during the event, although respondents conducted a high number of preparedness activities. A sizable portion of the sample conducted extended trips during the PSPS event days, whereas a small number evacuated to a destination overnight. Respondents received relatively clear information from multiple communication methods, indicating substantial information about the events. Modeling results found that power loss was a driver in travel behavior change, whereas demographics indicated heterogeneous responses within the sample. The paper concludes with a discussion of key takeaways and suggestions for research in this nascent field.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".