Where to plug in? Assessing the users’ preferences for EV charging location
Bibliographic record
Abstract
This study investigates individuals’ electric vehicle (EV) charging location preferences in the Okanagan region of British Columbia. Data comes from the British Columbia Activity Time Use Survey conducted in 2023, which collected individuals’ ranked preferences for charging their EVs in the following location alternatives: home, work, grocery stores, shopping malls, en-route, gas stations, and other locations. A random parameter rank-ordered logit model is employed to capture the relative preferences for different charging locations. The results reveal that individuals’ socio-demographics, travel attributes, built-environment characteristics, and accessibility measures significantly influence EV charging location preferences. For example, higher-income individuals show a higher preference for charging at home and workplace. Residents of detached houses prefer home and workplace charging over grocery stores and shopping malls. Apartment dwellers show a higher preferences for charging their vehicles in grocery stores, shopping malls, gas stations, and other locations. Additionally, individuals traveling longer distances daily are likely to have higher preferences for charging their EVs in shopping malls, en-route, and gas stations. The proximity of charging stations and land use mix, also play a critical role in influencing charging location preferences. A higher number of charging stations near home is found to reduce the preference for home charging. On the other hand, a higher land use mix around workplaces, indicating the availability of diverse amenities, reduces the preference for workplace charging. Providing community-based charging facilities might be able to accommodate the EV charging needs of these individuals. Nevertheless, the findings of this study provide valuable insights for policymakers and planners regarding user preferences for charging which will help in strategic investments, planning, and EV charging infrastructure development.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".