Coins, Cards, or Apps: Impact of Payment Methods on Street Parking Occupancy and Search Times
Bibliographic record
Abstract
City dwellers often struggle with on-street parking in many cities, where they generally need to pay for parking in advance. However, drivers usually cannot accurately foresee how much parking time they need. Compared to traditional payment methods, that is, cash and credit card through on-site meters, mobile payment applications provide more flexibility: drivers can adjust their parking sessions remotely if a longer stay is in need. Utilizing data from an online survey and high-resolution transaction records provided by a municipal agency in a densely populated North American city, we analyze how different payment methods and hourly parking prices affect drivers’ parking behavior, street parking occupancy, and search time to find an available parking spot. Our findings reveal that mobile payments facilitate shorter parking duration, which in turn improves the turnover rate of parking spaces and reduces the overall search time. Furthermore, we observe that a driver’s parking behavior is not solely determined by price or payment method but rather by the interaction of both factors, making it essential for any policy analysis to consider this interplay. In particular, mobile payers are more sensitive to price changes than credit card payers, whereas cash payers are identified as the most sensitive to price changes. To provide further guidance to municipalities, we simulate different pricing mechanisms and show that progressive pricing and mobile payment adoption, along with pricing strategies, significantly impact both search time and occupancy compared to constant pricing.
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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".