A Novel Reservation and Allocation Approach of Shared Parking Slots considering the Noncritical Aisle Space
Bibliographic record
Abstract
Urban areas are experiencing a substantial increase in parking demand, which creates an imbalance due to the limited availability of parking facilities. To address this issue, effective parking management is necessary. Shared parking is an alternative solution that utilizes private plots which are vacant during the day to serve the parking needs of users engaged in nearby activities. While current parking management approaches have achieved high utilization rates for available parking slots, the use of aisle space in parking lots can alleviate oversaturated parking demands, especially during peak periods, for users with relatively short parking durations. This study aims to model the noncritical aisle space with time-space constraints in the shared parking slot allocation problem. We propose a programming model that maximizes the profit of the platform based on a reservation and allocation platform. Numerical experiments demonstrate that utilizing the aisle space can enhance the revenue and enable the platform to accommodate more requests that meet the requirements. Moreover, the presence of aisle parking slots may potentially result in slightly lower turnover rates of normal parking slots.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".