Connected-Vehicle-Based Traffic Signal Control Strategy for Emergency Vehicle Preemption
Bibliographic record
Abstract
Connected Vehicle (CV) technologies such as Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) promise major benefits in both mobility and safety applications. One of the CV applications is connected traffic signal preemption for emergency vehicles enabling the rapid movement of emergency vehicles in urban arterials. This paper describes an innovative signal control strategy proposed to decrease Emergency Vehicle Response Time (EVRT). By employing V2I communication and IEEE 802.11p beaconing concept as well as the predicted queue length, traffic signals are adjusted adaptively to provide an early green at the right time so that the queue at the downstream intersections can be served just in time for the arrival of an emergency vehicle. The strategy is implemented in the microscopic traffic simulator, SUMO and evaluated using the City of Toronto network. In addition, a Python-based program is developed to link the control strategy to SUMO for simulating the traffic with intelligent traffic signals. The simulation results show a significant reduction in EVRT using the proposed 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".