Controlling on-street parking demand using sensitivity analysis: A case study at Kolkata
Bibliographic record
Abstract
On-street parking is one of the most important aspects of the transportation system in any central business district (CBD) worldwide. Managing the on-street parking demand is an important issue in transportation planning, especially for metropolitan cities. Every car owner prefers to park their vehicle as close as possible to destination to minimize the walking distance, leading to overcrowded. The objectives of this study are to find out important parameters on on-street parking, generating a parking demand estimation model and to carry out sensitivity analysis to obtain most sensitive parameter(s) for demand estimation. The data are collected from field survey. Four CBDs of Kolkata, viz. Camac Street, Dalhousie, Gariahat and Park Street have been selected as case study areas in this study. The estimated demand is found to be much higher than the present supply. The forecasted demand is also estimated. Statistical software like SPSS is used for analysis.
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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 | 0.001 |
| 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".