A Simulation-Based Comprehensive Analysis for Traffic Efficiency and Spatial Distribution of Risks in Short Weaving Area of Municipal Interchange
Bibliographic record
Abstract
The weaving area is an important junction that affects the efficiency and traffic safety at the municipal interchanges. To explore the factors affecting traffic efficiency and the spatial distribution characteristics of traffic risks in the weaving areas of municipal interchanges, this study employed a microscopic traffic software of Vissim to construct a simulation model of weaving areas and evaluate the impact of road and traffic design parameters in the short weaving on traffic efficiency and risks, which includes ramp delay, mainline through lane (TL) traffic delay, average ramp speed, TL average speed, and collision rate. Through variance analysis via a large number of simulation scenarios, the weaving length is identified as the most important factor affecting traffic efficiency and risks in the short weaving area. Subsequently, three different types of weaving lengths with 350 m, 450 m, and 550 m were set to conduct the sensitivity analysis based on four performance indexes of mean acceleration and deceleration, acceleration range, deceleration range, and speed standard deviation as the representative variables of spatial risk distribution. The simulation results illustrate that a shorter weaving length has a significant influence on risk distribution, especially the highest risk probability at the positions after three-quarters of the inner and outer lanes in the short weaving area at the municipal interchange. Finally, this study verified the traffic risk reduction method of having traffic safety facilities and traffic organization at the complex interchange with double-entry and single-exit weaving areas in the city of Guangzhou, China. The research proposed a method to analyze the influence of the design parameters in the short weaving area on traffic efficiency and safety and provided a reference for the risk spatial distribution analysis and improvement in the short weaving area.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".