VISSIM-Based Simulation and Analysis of Upstream Segments in Ramp Areas for Optimizing Vehicle Group Lane-Changing Behaviors
Bibliographic record
Abstract
The traffic congestion in ramp areas is becoming increasingly prominent. In the upstream segments of ramp areas, effective management and control of lane-changing behaviors can improve the road capacity and make full use of the existing road resource. With the continuous development and application of connected vehicle technologies, lane-changing behaviors can be performed by vehicle groups. Under a connected vehicle environment, the lane-changing behaviors by vehicle groups are controlled in the upstream segment in a ramp area, and the lane-changing behaviors can be completed prior to entering the ramp area. Finally, lane-changing strategies are optimized and identified. VISSIM simulates these proposed strategies. This paper considers the delay as the output index for analyzing and comparing various strategies. The results demonstrate that the delays of different lane-changing strategies are also different. If the delays of ramp areas are to be substantially reduced, it is necessary to continuously optimize the lane-changing strategies by vehicle groups in the upstream segments. This optimization of lane-changing strategies will effectively regulate drivers’ lane-changing behaviors, improve road safety, and increase traffic capacity.
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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".