Prospect Theory-Based Two-Sided Satisfied and Stable Matching Mechanism for the Shared Parking Slots Problem
Bibliographic record
Abstract
The contradiction between the dramatic increase in the aggregate number of automobiles and the short supply of parking spaces leads to parking difficulties. Sharing mode helps improve the efficiency of existing parking spaces, increase resource utilization, and alleviate the difficulty of parking. This paper focuses on the matching mechanism in the shared parking slots problem, which involves three agents: shared parking suppliers, shared parking demanders, and shared parking platform. We propose a prospect theory-based two-sided satisfied and stable matching model (PT-TSSM) with two objectives to maximize the satisfaction degree of both shared parking demanders and shared parking suppliers. Numerical experiments are illustrated to demonstrate the efficiency of the proposed model. Moreover, the PT-TSSM model is compared with the other two shared parking mechanisms. The proposed model considers the satisfaction degrees of both shared parking demanders and suppliers, while first book first serve (FBFS) cares only one side of the participants. And compared with deferred acceptance (DA), our model not only takes two-sided stable matching into account but also considers the satisfaction degree of all the demand and supply participants, which obtain a two-sided satisfied and stable matching scheme.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".