A Priority Algorithm to Control the Traffic Signal for Emergency Vehicles
Bibliographic record
Abstract
Signal preemption disrupts normal traffic signal to allow emergency vehicles to pass through the intersection more safely and quickly. In emergency situations, EVP (Emergency Vehicle Preemption) offers a faster response to the sufferer. Despite this lifesaving advantage, conventional preemption has some problems which need more attention. Increasing delay on overall traffic and failure to prioritize among conflicting preemption requests are two important issues to address. This paper presents a priority signal control algorithm to deal with these issues. We have used TSP (Transit Signal Priority) techniques to improve the EVP system. TSP is a proven strategy to provide a better quality public transit operation in urban areas. Our proposed algorithm adjusts signal phases using TSP techniques to serve an emergency vehicle. These techniques help us to alleviate the impact of EVP on general traffic. Experiments have been conducted using the VISSIM microscopic traffic simulator. Results show that the proposed traffic control algorithm reduces overall traffic delay by up to 8% compared to conventional EVP system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".