Exploratory analysis of an optimal variable speed control system for a recurrently congested freeway bottleneck
Bibliographic record
Abstract
Summary This study presents two models for proactive variable speed limit (VSL) control on a recurrently congested freeway segment. The proposed model uses embedded traffic flow relations to predict the evolution of congestion patterns over the projected time horizon, and then computes the time‐varying optimal speed limit to smooth traffic flows. To contend with the uncertainties associated with drivers' responses to VSL control, this study has also proposed an advanced model that further adopts Kalman Filter to enhance the traffic state estimation. Both models have been investigated with two control objectives—travel time minimization and speed variance minimization. Our extensive simulation analysis with a VISSIM simulator, calibrated with field data from our previous VSL field demonstration, has revealed the benefits of the proposed VSL control models. Also, the experimental results indicated that the proposed advanced models with both control objectives can significantly reduce the travel time over the recurrent bottleneck locations. With respect to several selected measure of effectiveness (MOEs), such as average number of stops and average travel time, the research results confirm that the control models with the objective of minimizing speed variance can offer the promising properties for field implementation. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".