Berth assignment planning for multi‐line bus stops
Bibliographic record
Abstract
SUMMARY In this paper, we study an important problem that arises with the fast development of public transportation systems: when a large number of bus lines share the same bus stop, a long queue of buses often forms when they wait to get into the stop in rush hours. This causes a significant increase of bus delay and a notable drop of traffic capacity near the bus stop. Various measures had been proposed to relieve the congestions near bus stops. However, all of them require considerable financial budgets and construction time costs. In this paper, with the concept of berth assignment redesign, a simulation‐based heuristic algorithm is proposed to make full use of exiting bus berths. In this study, a trustable simulation platform is designed, and the major influencing factors for bus stop operations are considered. The concept of risk control is also introduced to better evaluate the performance of different berth arrangement plans and makes an appropriate trade‐off between the system's efficiency and stability. Finally, a heuristic algorithm is proposed to find a sub‐optimal berth assignment plan. Tests on a typical bus stop show that this algorithm is efficient and fast. The sub‐optimal berth assignment plan obtained by this algorithm could make remarkable improvements to an actual bus stop. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".