A rule‐based model for integrated operation of bus priority signal timings and traveling speed
Bibliographic record
Abstract
SUMMARY This paper focuses on integrated operation of signal timings and bus speed to provide priority to buses at isolated intersections when real‐time adjustment of bus speed is available (e.g., through Connected Vehicle). Most previous work assumes that the speed of a bus is given as an exogenous input and focuses merely on optimization of signal timings. The bus‐passing window and the bus‐arriving window are defined with respect to the real‐time signal status and bus arrivals to capture explicitly the interaction between bus speed and transit priority signal timings. A set of integrated operational rules is developed on the basis of these windows for buses with and without schedule deviation with the objective of minimizing bus schedule deviation, bus fuel consumption, and emissions. Four subsets are included: impacts of preceding bus analysis rules, priority requests generation rules, priority passing rules, and speed adjustment without priority rules. A VISSIM‐based simulation platform was designed and used for simulating and evaluating the proposed method. Extensive experimental analyses have shown that the proposed rule‐based integrated operational approach outperforms the no priority and conventional priority strategies (no bus speed adjustment) in terms of reducing bus delays, improving schedule adherence, saving energy, reducing emission, and minimizing the impacts on general traffic. The sensitivity analysis has further demonstrated the potential of the proposed approach to be applied in real‐time bus priority control system under different levels of transit and traffic demand. Copyright © 2012 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".