Ramp Metering Enhancements for Postponing Freeway-Flow Breakdown
Bibliographic record
Abstract
Ramp management is one of several functions performed to optimize traffic operations along a freeway. Existing ramp metering algorithms have been shown to be successful in increasing freeway throughput, and reduce overall travel time. Recent research has shown that there is a correlation between the number of vehicles arriving in clusters from the ramp and the probability of breakdown (i.e., beginning of congestion) at the ramp merge. The objective of this research was to develop enhancements for ramp metering strategies so that they can postpone the breakdown and reduce congestion at freeway facilities with recurring congestion. This research first developed a process for obtaining breakdown probability models for existing critical ramps. Next, it proposed specific enhancements to existing ramp metering algorithms which incorporate probability of breakdown models. Proposed enhancements are presented for two algorithms: the Minnesota Stratified Ramp Metering Algorithm (SZM), and the Ontario COMPASS algorithm. Simulation was used to replicate these algorithms and evaluate the proposed enhancements. The results of these experiments showed that the enhancements are effective in postponing congestion at the two sites evaluated by 17-35 minutes.
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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 | 0.002 |
| 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 | 0.001 |
| 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".