Real-Time Integrated Limited-Stop and Short-Turning Bus Control with Stochastic Travel Time
Bibliographic record
Abstract
In a traditional transit system, passenger arrival time and bus running time are typically random and uncoordinated. This randomness gives the appearance of unbalanced passenger demand and unreliable transit services. Therefore, this paper proposes a real-time control method for bus routes. In our method, buses skip some stations and turn back at appropriate stations, in order to balance passenger demand along the bus route and improve the overall transit service. Our real-time control method considers the typical changes in passenger demand and the stochastic travel time of buses. In this paper, the number of controlled vehicles at any given time is determined, and the bus holding time at the turn-back station is adopted. When implemented correctly, the optimal scheme indicates which stations should be skipped, where it is suitable for buses to turn back, and how long the holding time should be at turn-back stations, which in turn will minimize the total cost of a transit system. This paper formulates such an integrated strategy, presents the solution method of the formulation, and proves the validity of the real-time control method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".