Modeling of On-Street Parking for 4-Wheeler in Urban CBD: A Case Study
Bibliographic record
Abstract
Parking of vehicles is one of the most important aspects of the transportation system in any central business district (CBD) worldwide. Indian metropolitan cities are also facing the same. Lack of off-street parking force the users to park their vehicle on-street leading to increase in on-street parking demand. The demand is also increasing due to the increase in the vehicular ownership and the poor quality of transit system. As a result, main carriageway width is reduced, flow is decreased and unnecessary congestion to traffic flow is being created. Managing the on-street parking demand is a very important issue in transportation planning, especially for metropolitan cities. The present study aims to estimate on-street parking accumulation profile, parking occupancy profile, parking attraction, on-street parking demand, Level of Service (LOS) of on-street parking and effect of mode shift on on-street parking demand. Kolkata is one of the largest and oldest metropolitan cities in India. Four CBDs in Kolkata - Camac Street, Gariahat, Dalhousie and Park Street are selected as the case study areas on the basis of the intensity and type of land use and on-street parking scenario.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".