Design of a Decision-Based Multicriteria Reservation System for the EV Parking Lot
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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