Capacity Estimation for Weaving Segments Using a Lane-Changing Model
Bibliographic record
Abstract
During peak periods, freeway bottlenecks can be activated by intensive lane changing at weaving segments, where merging and diverging areas are in close proximity. This weaving phenomenon has a major impact on capacity. Much research has been devoted to investigating capacity estimation models for weaving segments. However, because of the model parameters, estimation models are difficult to adopt directly in active traffic management strategies to estimate real-time maximum discharge flow. To this end, this study defined a linear optimization problem to solve weaving capacity and then established a lane-changing model to constrain the weaving flows. The proposed method was evaluated and analyzed for sensitivity with field data from two weaving segments on Whitemud Drive, Edmonton, Alberta, Canada. The capacity estimates from the proposed model were consistent with that from the Highway Capacity Manual 2010 model and with field observations. Moreover, it was also observed that the weaving capacity was sensitive to weaving maneuvers. Finally, the proposed method was applied to estimate the real-time maximum discharge flow rate; the estimates matched field measurements. These findings could lead to implementations in designing optimal traffic control strategies.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".