Sensitivity Analysis of Freeway Capacity at a Complex Weaving Segment
Bibliographic record
Abstract
Freeway mainline and ramp flows interact at weaving segments. During traffic peaks, drivers' intensive weaving maneuvers yield frequent interferences between vehicles, which disturb flow and reduce freeway capacity and safety. Thus, effective freeway operation strategies require weaving capacity estimations to assess real-time traffic states. Generally, existing weaving capacity estimation methods are classified as theoretical or empirical. However, some of the parameters adopted in these existing methods are insufficient as inputs for freeway operation. This study aimed to fill that gap. The study began with a determination of all of the possible weaving parameters that may be used in traffic control. Then, a micro-simulation model was calibrated with field data to replicate the capacity of a two-sided weaving segment. With the calibrated micro-simulation model, a sensitivity analysis was systematically performed to determine the weaving parameters' potential impact on weaving capacity. The results showed that the simulated weaving segment capacity is highly sensitive to speed, while the variations in bottleneck capacity reductions are sensitive to traffic flow rates and their proportions. Finally, an empirical model for capacity estimation was established based on the sensitivity analysis results. The developed model could be applied to dynamic freeway operation strategies to accurately estimate traffic states.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".