The Price Mechanism Analysis of Parking Fees on Economic Perspective
Bibliographic record
Abstract
Growing number of vehicles brings sever negative external effects to cities such as traffic congestion and tail gas pollution, especially to big cities. However it is the tail gas pollution which is a significant factor for the formation of haze. Gradually serious congestion and haze problems force us to face and solve the contradiction between growing demand for motor vehicles and the scarce resources of urban traffic. This paper analyzes residents’ tenure and travel vehicle cost by constructing the parking price model and the linkage mechanism of parking fees-vehicle cost from the vehicle and complementary relationship of parking space in economic perspective. And this condition will affect people’s desire of shopping and vehicle travel indirectly. From the consumer perspective, it will affect their choice to effectively control of motor vehicle growth, decrease usage amount, and promote the allocation of urban transportation resources. Finally, we can get a policy enlightenment that city managers can use price mechanism of parking fees to improve the efficiency of the urban traffic from several aspects, like speeding up the property rights reform bus, establishing adjustment system of dynamic price, implementing the policy of differentiation parking, optimizing the bus system and appropriately limiting parking supply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".