Design of a Decision-Based Multicriteria Reservation System for the EV Parking Lot
Bibliographic record
Abstract
In metropolitans, the problem of finding available parking slots has changed as finding available parking slots having charging stations due to increasing electric vehicle (EV) deployment. Smart management systems can be used in this manner for obtaining an optimum parking slot in EV parking lots (PLs) considering EV users’ preferences. This article proposes a smart reservation system considering the behavior of EV users, parking slot availability (PSA), state-of-charge (SoC) value of EVs, and PL usage history of EV users. In order to handle weighting the behavior of EV users according to a comprehensive criteria comparison, the analytical hierarchy process (AHP) from multicriteria decision-making (MCDM) techniques is used in the smart reservation system. Thereafter, the proposed ranking function is presented to develop the mentioned quality-of-experience (QoE)-based charging slot allocation considering the reservation requests of EV users sent via a mobile application and to accept the optimal EVs in accordance with the weights assigned by AHP. The proposed concept is tested under different cases generated by changing the individual importance degree of EV user’s criteria. The different case studies demonstrate the effectiveness of the proposed decision-based multicriteria reservation system in terms of EV users’ acceptance ratio. Simulation results show that not only the importance degree related to the EV users’ criteria has an important effect in accepting appropriate EV users but also PSA management is another vital criterion especially in peak-load hours.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".