Development of Parking Demand Model for Private Hospital in Developing Country (Case Study of Denpasar City, Indonesia)
Bibliographic record
Abstract
Denpasar City is the capital of Bali Province and the center of activities in Bali, Indonesia. The population continue to increase with the annual growth rate of 2%. As the number of population increase, the number of facilities including health facility also continue to increase. The traffic volume is predominated by private motor vehicle (where 80% is motor cycle) as lack of public transport service available. The trip attraction to hospital increases, however parking spaces provided are very limited. As the results the visitors usually park their vehicles on street around the hospital. This has caused a significant reduction in the road capacity. Therefore, it is required to accurately estimate parking demand both for car and motor cycle. The objectives of this study are to analyze parking characteristics and to develop parking demand models for car and motor cycle. Five private hospitals were considered in this study. Parking data were collected and used to model parking demand based on simple and multiple liner regression models. The results of this study indicated that the parking index for all private hospitals has exceeded 1. The number of beds for room class 1 was found to be the main predictor for parking demand for car. However, the number of hospital’s employees was found to be the best predictor for parking demand for motor cycle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".