On-Street Parking Demand Estimation in Urban CBD using FI and CF Model: A Case Study – Kolkata, India
Bibliographic record
Abstract
Objectives:To estimate the on-street parking demand in the urban Central Business Districts (CBDs). Methods/Statistical Analysis: To achieve the goal, the study formulates two parking demand estimation models i.e., the fee index (FI) model and the cost factor (CF) model, based on regression analysis using SPSSStatistical Package for the Social Science. FI Model estimates the on-street parking demand where the transit system is absent. On the other hand CF model estimates the demand by considering the mode shift from the private vehicle (PV) users to the public transit (PT). Findings: Priority wise requirements for selecting PT are found out in this survey. The existing demand in the both selected CBDs of Kolkata, viz. Dalhousie and Gariahat is found to be much higher than the present parking supply. FI Model shows that, the demand will satisfy the existing supply if unit FI can be achieved. CF model explain that, the transit fare need to be increased by 52% and 26% for Dalhousie and Gariahat area respectively to meet the demand with the existing supply. It is also found out that, the on-street demand is less in transit oriented CBDs. The forecasted demand is reduced by 69% and 71% and by 63% and 59% than the present demand using CF model and the FI model respectively. In this study, it has been attempted to evaluate the on-street parking demand and such type of works has not been found out by the authors particularly in India which make it a pioneer study for others. Application/Improvements: The users need to be shifted from PV to PT immediately and the government must take necessary actions to introduce sufficient transit service to counter the on-street parking problem. Keywords: CBD, On-Street Parking Demand, Parking Demand, Parking Supply, Parking Demand Model
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".