Evaluation of Spatiotemporal Transit Accessibility: Weighted Indexing Using the CRITIC‐MCDM Approach and Performance Gap Analysis
Bibliographic record
Abstract
Transit performance is greatly influenced by its accessibility, which considers the spatial distribution of transit facilities with different periods of operation. The present study analyzes the spatiotemporal variation in transit accessibility and proposes a modification to enhance the evaluation process. The proposed modification involves assigning weighted indexing to the public transport coverage index (PTCI) using the CRITIC (criteria importance through intercriteria correlation) MCDM technique. The indicators exhibit temporal and spatial variations based on network and operational characteristics, with temporal variations relying on the number of scheduled transits and spatial variations influenced by the network and other operational attributes. The case study conducted in Surat, India, reveals that areas such as the city center and inner fringe have a higher concentration of scheduled transits and bus stops. However, demand fulfillment, measured by the offered seat capacity per population, is relatively low in most zones. To prioritize areas for resource allocation and policy implementation, the use of “RAdial REferenced Scatter QUAdRant (RARE SQUARE) Performance” charts are developed, which provide a straightforward tool to validate findings. The study highlights a low relative transit demand in the city, resulting in a mode share of approximately 2.5%.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".