DynaTAM: an online algorithm for performing simultaneously optimized proactive traffic control for freeways
Bibliographic record
Abstract
Modern proactive control algorithms can perform real-time traffic state prediction and help determine control plans for active traffic control measures, such as ramp metering (RM) and variable speed limit (VSL). In this study, a control algorithm, DynaTAM (Dynamic Analysis Tool for Active Traffic Demand Management), is proposed for prediction-based, integrated optimization control. DynaTAM considers the correlations between the implemented control measures and determines the control plan for both RM and VSL at the same stage of optimization. The algorithm framework possesses a candidate control plan generator, which reduces the computational load of future optimizations and eliminates much of the uncertainty in the system performance. A field-data-based simulation study with different control scenarios is conducted to evaluate the performance of DynaTAM. The DynaTAM control algorithm is shown to not only effectively perform integrated and coordinated control using both RM and VSL, but also significantly improve the efficiency of traffic operations during peak hours.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".