Traffic-Responsive Signal Control at Intersections Using Real-Time Data of Vehicles Connected via V2X Communication
Bibliographic record
Abstract
The positive effect of traffic-responsive signal control can be assured when real-time traffic data is reliable, but data reliability may be an issue that depends on the number of probe vehicles equipped with navigation devices or smartphones. However, there is a high chance of improving reliability with the recent deployment of connected vehicles (CVs) that use the vehicle-to-everything (V2X) communication data. Therefore, this paper proposes a traffic signal control strategy that utilizes V2X communication data obtained from CV operations, which is called the capacity waste reduction (CWR) strategy. In this strategy, vehicle queues on each road lane as an intersection approaches are initially estimated using V2X data. Then, the signal control algorithm determines the duration of the green signal for the currently applied phase based on the estimated vehicle queues. Furthermore, the strategy includes an algorithm for active priority signal control for the vehicles of bus rapid transit systems. The efficiency of the provided control strategy is tested with the VISSIM microsimulation program at different levels of the market penetration rate (MPR) of CVs. Based on the results of the experiment, the proposed strategy shows positive effects in both decreasing travel delay and increasing traffic flow even at the low levels of MPR of CVs. The results of the proposed strategy can be used as the base data for the development of smart intersection operations.
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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 | 0.000 |
| 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 | 0.000 |
| 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".