Different Demand for Charging Infrastructure along a Stretch of Highway: Italian Case Study
Bibliographic record
Abstract
Electric Vehicles (EVs) play an important role to reach the global ambitious climate and air quality goals. The timely implementation of adequate EV charging infrastructure is critical to the success of the ecological revolution. However, at the same time, EV adoption is the main driver for the business case of EV charging infrastructure. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors which influence the charging demand of EVs. This paper aims to evaluate the charging infrastructure on highways by considering several factors that impact electric vehicle deployment. To do this, a simulation model is developed. An agent-based approach, according the AnyLogic software tool, is used to emulate the human behavior and its impact on load demand due to EV charging. Furthermore, a discrete event method is applied for simulating the queue in the charging stations. Therefore, an electric vehicle routing algorithm is developed, which analyses the current status of the charging infrastructure along the highway considering an EV traffic flow travels in the highway in one day.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".