Emergency Management Plan for Electric Vehicles During Floods Using Daily Routing Patterns
Bibliographic record
Abstract
Canada has witnessed damaging flooding events in the summer of 2023. During such events, trips should be rerouted to safer routes than those commonly used in typical day operational scenarios. The objective of this paper is to 1) prioritize Electric Vehicle (EV) trips while developing the safest routing plan for EVs, and 2) determine alternative safe locations for highly-used EV Charging Infrastructure (EVCI) during day-to-day operational scenarios. The Flood Susceptibility Index national map is used as an indicator of flood severity. Two-level frequency assessment matrices are developed to rank and prioritize EV trips and EVCI locations. Given limited resources, the nearest locations to the most critical EVCI locations are then determined on EVs’ safest routes. A case study is investigated in Halifax Municipality, Nova Scotia, where severe flash flooding events occurred in the summer of 2023. The findings of this paper will support policymakers while 1) planning EVCI during floods by identifying locations needing EVCI installation on the safest routes and 2) developing rerouting plans for EVs by comparing the consequences of different rerouting alternatives considering the availability of EVCI. These applications serve as a compromise between safety and mobility while developing traffic management plans for EVs during flooding 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".