Analysis of overlapping origin–destination pairs between bus stations to enhance the efficiency of bus operations
Bibliographic record
Abstract
Public transit has a significant impact on minimising traffic congestion and reducing the cost of travelling in urban areas. It is necessary to evaluate the efficiency of the public transit operation in response to the individual traveller's demands for transit. This study aims to analyse the demand for transit with overlapping origin–destination ( OD ) pairs to enhance the efficiency of transit operations. To achieve this, disaggregated‐level travel demand data, i.e. individual traveller's data are collected from an automatic fare collection system called smart card. The Kneedle algorithm is used to calculate the knee point of travel demand. The overlapping OD pairs, which are higher than the knee point value, are calculated and displayed in a map format. On the basis of the overlapping OD pairs, the demand‐based overlap index for each bus route is defined to evaluate the efficiency of bus operations. The proposed method is applied to six districts with higher transit demands than other districts in Seoul. On the basis of the results, discussion on the action plans to enhance the efficiency of bus operations are presented. The method proposed in this study contributes to improving the efficiency of the bus system by reflecting individual users’ travel demands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".