Could the Incentive Policy of Shared Parking Be Sustainable? The Viewpoint of Personal Risk Aversion
Bibliographic record
Abstract
Using an incentive measure to encourage people to share their private parking spaces could be an effective strategy for urban parking problems. This paper discusses an innovative mechanism of shared parking, “FlexPass,” which applies a reverse auction in which drivers propose bids in line with their individual expectations to share their idle parking spaces. The auction mechanism, hypotheses on bidding process principles, the competitive environment, and the risk-averse decisions of providers with regard to parking spaces are analysed to explore the sustainability of the economic benefits obtained for FlexPass parking spaces. A total of 216 respondents from the University of California, Berkeley, were invited to participate in bidding in an actual survey during their daily use of parking spaces. The analytical results show that operational rules based on risk aversion can enable profit-seeking with a bounded capability to obtain considerable economic benefits and release parking resources in an environment of demand competition. Particularly in some scenarios, FlexPass would sacrifice a certain monetary income to ensure the perceived benefits of parking space providers. With the improvement of people’s enthusiasm for participating in shared parking, the benefits to individuals and parking lots would be further enhanced, suggesting that our mechanism can operate sustainably over the long term. These findings are helpful for policymakers to formulate feasible shared parking policies from the perspective of monetary incentives.
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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 | 0.001 |
| 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 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".