Sensitivity Analysis of Fleet Size for Dynamic Headway-Based Control Method Performance in terms of Passengers’ Experience
Bibliographic record
Abstract
The headway-based control method is usually used to regulate the bus headways to improve the bus operation. However, these control methods only focus on equalizing bus headways, neglecting analysis for STD (standard deviation) of passengers on the buses. Therefore, in order to fix this gap, this paper analyzed performance of the control method in terms of regulating bus headways and number of passengers on the buses under variable number of buses on the route. First, as an enhanced version of headways-based control method, a coordinated control method based on the self-equalizing bus headways concept involving bus holding and stop-skipping is proposed. A discrete system is formulated to describe the bus operation, and the coordinated control method is imbedded into it. In order to reflect the performance in regulating bus headways and the number of passengers, two indexes are formulated, respectively. Finally, a set of numerical analysis was conducted. The test results showed that the regulated bus headways can make the number of passengers on buses equalized. As a by-product, the headway-based control method can make the bus capacity be well utilized by equalizing bus headways on the bus route.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".