Parking Space Reservation Behavior of Car Travelers from the Perspective of Bounded Rationality: A Case Study of Nanchang City, China
Bibliographic record
Abstract
For travelers who inevitably use motor vehicles, in the case of limited parking spaces, reserving parking spaces in destination in advance helps reduce the time and emissions of searching for parking spaces and alleviate road traffic pressure. From the perspective of bounded rationality, this paper comprehensively considers the impact of traveler’s personal attributes and behavioral characteristics on parking reservations. The data processing analysis shows that the traveler’s age, gender, monthly income, and other characteristics have a certain impact on the parking reservation choice behavior. Reservation price is the key factor affecting the parking reservation policy. Travelers show different value perceptions of the reserved price of parking spaces, and this process has been verified to be roughly the same as the prospect theoretical model. As the reference point for highest reservation price becomes larger, travelers tend to choose to pay less than the ideal reservation price and become more sensitive to losses. It can be found from the model functions and survey data that the ideal reserved parking space price in the survey area is 5 yuan per hour which equals the normal parking fee, and the ideal parking reservation time is less than 2 hours. The research results provide a basis for formulating reasonable parking reservation schemes and parking policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".