Cellular automaton simulation of vehicles in the contraflow left‐turn lane at signalised intersections
Bibliographic record
Abstract
To improve the capacity of signalised intersections for left‐turn vehicles, an unconventional left‐turn design named the contraflow left‐turn lane is applied at intersections in several cities of China. The main concept of the design is to provide more capacity for left‐turn vehicles by dynamically making use of the adjacent opposite lanes. In this study, a cellular automaton model that simulates left‐turn traffic flow at a signalised intersection with a contraflow left‐turn lane was developed and verified by field data. Rules for vehicles entering the contraflow left‐turn lane were proposed, and various factors including the vehicle type, driver type, and the ratio of U‐turn vehicles were considered. The simulation results showed that the contraflow left‐turn lane could increase the capacity of the intersection and decrease the delay of left‐turn vehicles. Also, the optimal match between the length of the contraflow left‐turn lane and the duration of the pre‐signal green light can be evaluated via simulation. This study can provide a reference for the actual application of contraflow left‐turn lanes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".