An archetypal routing network model to help identify potential charging locations for long-haul electric vehicles in Ontario, Canada
Bibliographic record
Abstract
Some estimates show long-haul transport trucks contribute as much as 10% of all Canada’s greenhouse gas emissions. Long-haul electric vehicles (LHEVs) or “electric big rigs” offer a potentially compelling option to mitigate these emissions. However, LHEV charging is expected to burden the power grid significantly more than charging smaller passenger electric vehicles. To date, there is very little research on the impact of charging such vehicles on power grids. The following study leverages conventional long-haul truck GPS data to develop an archetypal routing network (ARN) model that can help identify candidate charging infrastructure locations in Ontario, Canada. Results suggest that based on historical LHEV travel patterns, most candidate charging station locations fall along critical road links in Ontario like Highway 401 and Highway 400. Subsequently, the additional electricity demand of these stations is estimated and compared with Ontario’s current electricity demand. Though the charging stations’ aggregate daily demand is smaller than Ontario’s overall demand, some of these stations’ hourly electricity demand during peak hours are great enough to put significant pressure on local infrastructures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".