Electric Vehicles and Resilient Evacuations: A Multi-Method Study of Charging Behaviour, Decision-Making, and Infrastructure Challenges
Bibliographic record
Abstract
The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for disaster response and evacuation planning. While EVs offer sustainability benefits and the potential to supply power back to the grid through vehicle-to-grid (V2G) technology, their reliance on electricity introduces unique vulnerabilities, particularly in large-scale evacuations where charging infrastructure is limited. With significant threats from extreme weather and other hazards, the effectiveness of vehicular-based evacuations hinges on the resilience of EVs, related infrastructure, and energy systems. This thesis investigates the use of EVs in disasters through (1) a systematic literature review on EV resilience, (2) a discrete choice analysis of intended EV user behaviour during wildfire evacuations, and (3) an agent-based model (ABM) simulating evacuation scenarios to assess electricity demand and infrastructure constraints. The literature review highlights gaps in EV-specific evacuation planning, including limited charging networks, grid vulnerabilities, and a lack of dedicated emergency response policies. The discrete choice analysis, based on a stated preference survey conducted in wildfire-prone regions of Alberta and British Columbia, reveals that prior hazard experiences, socio-demographic factors, and risk perceptions strongly influence EV evacuation behaviours. The results suggest that targeted grid enhancements and strategically located charging stations could effectively manage the anticipated demand spikes. To further explore these dynamics, an ABM simulation was developed to model EV evacuations in a case study of Canmore, Alberta and analyzed four scenarios to test response differences after various parameter changes. The results highlight the risks of concentrated demand at charging stations (leading to severe delay) and the value of time-minimizing decision-making and increasing charging speeds to drastically reduce delays. Policy interventions—such as strategically placed temporary charging stations, faster charging stations, and incentivizing charging away from home—could mitigate these challenges and improve evacuation outcomes. Findings from this thesis provide recommendations for policymakers, emergency planners, and transportation agencies, including expanding charging infrastructure along evacuation routes, developing public-facing EV-specific evacuation plans, and leveraging smart charging and demand response programs. This thesis contributes to the growing body of research on electrified mobility and disaster resilience, providing insights to support safer and more efficient evacuations in an increasingly electrified transportation landscape.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".