Evaluation of Residential Parking Spots Sharing Effects Based on Practical Experience
Bibliographic record
Abstract
Making residential parking spots available to the public has become popularized in recent years. The sharing of residential parking spots can promote the further use of parking space and enhance the utility of parking resources in urban metropolitan areas. However, little is known about the relationship between spots’ physical or temporal factors and rental effects from practical experience. This study aims to evaluate the effects of residential parking spot sharing from both individual and social benefit perspectives. One-year real behavioral records concerning parking spots’ owners and borrowers were obtained, and the field survey of various parking spots’ physical characteristics was conducted. Two Partial Least Squares Structural Equation Modeling (PLS-SEM) models emphasizing the individual and societal points of view were adopted. Results revealed that the spots’ physical factors, including spot type, visibility, ease of parking, and distances to major surrounding buildings, along with owners’ sharing willingness and preferences, tend to pose significant influences on the rental effects from both individual and social benefit perspectives. Some differences were also discovered between the two models. For the individual model, owners’ sharing willingness was the dominant factor affecting the parking spots’ sharing effects, while for the social model, parking spots’ physical characteristics appear to be more important in determining the sharing effects. Based on these findings, suggestions were discussed to promote residential parking spot sharing and increase the benefits of sharing to individuals and society.
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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 | 0.001 |
| 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".