Identifying EV Bad Behaviour: The Parking Lot Usage Efficiency (PLUE) Metric
Bibliographic record
Abstract
We observe that EV charging stalls are often underutilized when drivers leave vehicles plugged in after charging ends, undermining station throughput, frustrating users, and complicating the business case for smart-parking investments. To address this gap, we introduce the parking lot usage efficiency metric (PLUE), defined as the ratio of active charging time to total plug-in time. PLUE quantifies post-charge occupancy on a continuous scale from zero (fully idle) to one (fully efficient), enabling objective classification of user behaviour and identification of inefficiencies. We demonstrate PLUE’s utility by applying it to a real-world multi-year campus charging dataset, revealing persistent idle intervals across multiple stations despite generally consistent charging demand. Parking Operators can then use the PLUE score together with operational KPIs like length-of-stay to better inform decision making such as matching infrastructure power levels to time parked, establishing overstay fines, and various other behaviour and revenue metrics.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".