Understanding spatial–temporal attributes influencing electric vehicle's charging stations utilization: A multi-city study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. • This study explored the utilization of electric vehicle (EV) charging stations. • It used data from eleven cities across three countries: the United States, Canada, and Scotland. • Up to five times higher utilization was observed across cities within the country. • Stations in the United States had a relatively higher predicted utilization than stations in Aberfeldy. • The day of the week and months of the year depicted consistent and mixed utilization patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it