A micro-simulation study on proactive coordinated ramp metering for relieving freeway congestion
Bibliographic record
Abstract
To relieve freeway congestion during peak periods, ramp metering (RM) is often implemented to control the input flow from onramps on freeways. Many studies focus on proactive coordinated RM controls; however, successful implementation of proactive RM control still requires a more accurate prediction model and a less complex control algorithm. To this end, this study tests a proactive RM approach in micro-simulation, with goals to improve network-wide travel time and traffic flow. A METANET-based dynamic traffic model was adopted as a prediction model within a predictive control framework. The evaluation revealed a 6.50% amelioration in total travel time on the mainline and a 2.52% reduction of total time spent in the network. The applied algorithm was compared with the HERO algorithm and implemented in various peak demand scenarios. This analysis could lead to efficient and effective field applications of proactive coordinated RM control to improve freeway operation.
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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".