A spatiotemporal GIS-approach for evaluating the safety of EV trips during wildfires
Bibliographic record
Abstract
Electric Vehicles (EVs) are evolving as a green transportation means to address climate change impacts. However, disruptive events could impede EVs' mobility even with sufficient driving range. This study thus fills a gap in transportation research by including the spatiotemporal impact of potential wildfires, represented by the daily maps of the Canadian Fire Weather Index (FWI), while calculating EVs' safest routes. A case study is conducted in Halifax, Nova Scotia, using three FWI scenarios representing high, medium, and low risk scenarios. EV trips are evaluated by 1) comparing the safest and shortest routes' FWI value and trip distance, 2) investigating the adequacy of existing EV charging infrastructure (EVCI) on those safest routes under different State of Charge (SOC) levels for EVs, and 3) comparing the safest routes in the different risk scenarios. The findings of this research have significant implications in supporting the resilience of transportation electrification by being one step towards proactive emergency planning during disruptive events.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".