Collaborative Strategies and Simulation of Vehicle Group Behaviors for Off-Ramp Areas
Bibliographic record
Abstract
With the increase of vehicle ownership and the rapid growth of urban traffic, the problem of congestion in the off-ramp area of the main expressway has become the main factor restricting overall section efficiency and inducing traffic accidents. This paper focuses on the problem of group collaborative lane-changing behaviors of off-ramp vehicles and through vehicles in off-ramp areas and proposes four kinds of vehicle group collaborative strategies based on different road space balance conditions. According to a three-lane expressway scene, a VISSIM-based simulation model is built and the optimization scheme is simulated and evaluated. The simulation results show that with the increase of traffic flow in off-ramp areas, a flow-balance strategy for downstream lanes where off-ramp vehicles merge with the outside lane in advance is more advantageous. When vehicles are leaving the main road, if traffic flow is heavy, the flow-balance strategy for lanes where off-ramp vehicles merge with the outside lane in advance (for example, the proportion of off-ramp vehicles in three lanes is 0 : 0 : 1) is better; otherwise, when the traffic flow on the main road is relatively small, the flow-balance strategy for lanes where off-ramp vehicles are distributed in lanes with different ratios (e.g., 1 : 3 : 6) is better. What is more, for future traffic management in connected vehicle environments, it can be concluded that collaborative vehicle lane-changing strategies with different traffic flow states can help to enhance 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".