Analysis of overlapping origin–destination pairs between bus stations to enhance the efficiency of bus operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 |
| 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 it