Integrated Variable Speed Limit (VSL) and Ramp Metering (RM) Control Strategy Based on Optimal Collision Probability Prediction
Bibliographic record
Abstract
Traffic congestion and collision problems had been focused widely by transportation researchers. Various traffic management strategies such as Variable Speed Limit (VSL), ramp metering (RM), etc., have been deployed to mitigate traffic congestion and collision. However, most previous studies focused on the impacts of VSL or RM control separately. Therefore, in this study, a model predictive integrated VSL and RM control strategy based on collision probability optimization was proposed. The proposed control strategy predicts future traffic states and collision probability, and considers the optimum VSL and RM input simultaneously by minimizing collision probability. To quantify the safety and mobility impact, the proposed integrated control was implemented in microscopic simulation and compared with VSL control, RM control and without any control scenario respectively. The results indicate that the integrated VSL and RM control strategy can improve safety by approximately 41% and mobility by approximately 13%. In conclusion, the proposed integrated VSL and RM control strategy was better than VSL control strategy and RM control strategy independently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".