Understanding spatial–temporal attributes influencing electric vehicle's charging stations utilization: A multi-city study
Bibliographic record
Abstract
Electric vehicles (EVs) are gaining popularity across the globe. Various initiatives are being implemented to ensure that most of the operating vehicles on public roadways are EVs by 2050. Such initiatives include the construction of charging stations to improve EV charging accessibility. The utilization of the charging stations has not been explored to a great extent, despite its importance in future installations in various cities. This study evaluated the EV station utilization across eleven cities in three countries: the United States, Canada, and Scotland. The Negative Binomial (NB) regression model was applied to understand the influence of the spatial–temporal factors on the daily utilization of EV charging stations. In addition to the overall analysis, country-specific analyses were also performed. It was revealed that there is a great variation in daily EV utilization across the cities in different countries and within the country. In fact, only stations in Crieff, Scotland, showed lower predicted daily utilization, while cities in the United States had over two times predicted daily utilization compared to stations in Aberfeldy, Scotland. Furthermore, the longer the station has been in service, the higher the daily utilization, although there was significant variation across cities. Further, the day of the week and months of the year depicted consistent utilization patterns for Scotland and the United States but showed mixed findings for Canada. The study findings can help planners and policymakers improve the allocation of EV charging stations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".