Traffic guidance measures based on vehicle-road cooperation under accident conditions
Bibliographic record
Abstract
When an accident occurs in a certain section of the expressway network, the traffic demand on the road cannot be met, and the travel efficiency of the expressway is greatly reduced. In this environment, appropriate induction measures can not only alleviate traffic congestion, but also ensure the minimum range of indirect impact of the accident. In the process of developing to the pure autonomous driving stage, the mixed driving of manually driven vehicles and autonomous vehicles is an essential stage, in this stage, when a traffic accident occurs, more efficient guidance measures need to be studied. In this paper, the OMNeT++ simulation framework and SUMO road simulation model are used to simulate how to induce traffic accidents in expressway sections based on vehicle-road cooperation. According to the simulation results, the induction scheme was evaluated and analyzed from the aspects of easing traffic congestion and improving traffic efficiency. The results show that the combined induction measures can effectively alleviate the congestion caused by the accident and improve the traffic efficiency.
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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".