Where to plug in? Assessing the users’ preferences for EV charging location
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
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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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 | 0.001 | 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