The Effects of Dynamic Pricing in the Search for Parking Availability and Economics
Bibliographic record
Abstract
Parking fee plays a crucial role in urban mobility as it affects cruising time when searching parking availability, walking time from parking slot to destination, and daily living expenses. In this study, we assess the economics of temporal dynamic parking pricing methods, Linear and Min-Max Rates, by incorporating empirical vehicular spatial coordinates and parking duration behavior of on-the-fly drivers. Utilizing urban car mobility movements as possible parkers, we investigate the behavior of drivers when selecting a parking space based on their allowance dictated by the Fixed Rate benchmark. We observed the space occupancy of chosen urban establishments and its generated revenues/losses. Our findings show that parking pricing schemes are implemented to regulate space occupancy of each establishment. As we further evaluate Fixed, Linear, and Min-Max Rates pricing ways, for a given parking duration default, the Fixed Rate benchmark tends to be more expensive than the Min-Max Rate pricing. The Linear Rate is always found to be the most expensive among the three for customers but most profitable for commercial establishments.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".