Bibliographic record
Abstract
Arnott and Inci [Arnott, R. and Inci, E., 2006. An integrated model of downtown parking and traffic congestion. Journal of Urban Economics 60, 418-442] developed an integrated model of curbside parking and traffic congestion in a downtown area. Curbside parking is exogenously priced below its social opportunity cost, and the stock of cars cruising for parking, which contributes to traffic congestion, adjusts to clear the market for curbside parking spaces. Denser downtown areas have garage as well as curbside parking. Because of economies of scale in garage construction, garages are discretely spaced. The friction of space confers market power on parking garages. Spatial competition between parking garages, as modeled in Arnott [Arnott, R., 2006. Spatial competition between downtown parking garages and downtown parking policy. Transport Policy 13, 458-469], determines the equilibrium garage parking fee and spacing between parking garages. Also, the stock of cars cruising for parking adjusts to equalize the full prices of curbside and garage parking. This paper combines the ingredients of these two models, hence presenting an integrated model of curbside parking, garage parking, and traffic congestion, and examines curbside parking policy in this context through a numerical example with parameters representative of a medium-sized US city. The central result is that raising the curbside parking fee appears to be a very attractive policy since it generates efficiency gains that may be several times as large as the increased revenue raised.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".