Real-Time Estimation of Saturation flow Rates for Dynamic Traffic Signal Control using Connected-Vehicle Data
Bibliographic record
Abstract
Existing adaptive traffic signal control (ATSC) systems rely on dedicated fixed-point sensors, such as inductive loop detectors or video cameras, for measuring traffic demands and discharge saturation flow rates. The cost associated with installation, operation, and maintenance of these sensors is one of the factors that limit the deployment of ATSCs. The emergence of connected vehicles (CVs), which continuously broadcast their speed, position, heading, and other information to other vehicles and to roadside infrastructure, provides an opportunity to reduce the reliance of ATSC on data from fixed sensors and potentially to reduce ATSC deployment costs. However, so that existing ATSC systems can operate by using CV data, a methodology is needed for estimating demands and saturation flow rate based on CV data instead of fixed sensor data. This paper focuses on estimating the time-varying saturation flow rate for individual lane groups at signalized intersections solely on the basis of CV data. The accuracy of a proposed methodology is quantified through microsimulation for a range of traffic conditions, lane group configurations, and levels of market penetration (LMP) of CVs. The analysis shows that the proposed methodology can capture temporal variations in the saturation flow rate caused by road incidents, queues spilling back from downstream bottlenecks, and lane closures. The evaluation results show that the mean absolute relative error of the lane group saturation flow rate ranged from approximately 2% to 9% when LMP = 20% and only 1% to 2% when LMP = 100%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".