Evaluation and Analysis Model of the Length of Added Displaced Left-Turn Lane Based on Entropy Evaluation Method
Bibliographic record
Abstract
As the number of vehicles continues to grow in China, the problem of urban traffic congestion gets more serious, particularly at intersections. As a new type of unconventional intersection, the displaced left-turn (DLT) intersection has been widely recognized to improve the efficiency of intersections with heavy left-turn traffic flows. Converting an existing intersection into an intersection with additional DLT lanes is a valuable solution. However, the studies on DLT intersections mainly focus on performance, applicability, and safety. The guidelines on the specific engineering design process mostly come from experience, and the study on the considering multifactor design method is still insufficient. Therefore, this paper proposed an evaluation and analysis model of the lengths of added DLT lanes based on the entropy evaluation method, in which VISSIM and Surrogate Safety Assessment Model (SSAM) software were adopted for simulation. A design process for the length of the added DLT lanes was proposed with this model. An urban intersection in Xi’an was taken as a case study, and the application of the model and the design process was studied in detail. After selecting four evaluation indicators, the model was applied to calculate and analyze the optimal length of the added DLT lanes under 45 different traffic volume combinations. The recommended lengths of different situations were within the range recommended in the guidebook published by Federal Highway Administration. The results of the case study proved that the model proposed in this paper was advanced, reasonable, and practical.
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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 | 0.001 | 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".