Evaluating Safety of Type-A Weaving Sections Using Geometric and Traffic Operational Factors
Bibliographic record
Abstract
The efficiency and safety of type-A freeway weaving sections in urban areas are constrained by recurrent bottlenecks. Limited space in freeway weaving sections cause traffic congestion and crashes during peak-hours. Various factors, including length of weaving section, continuity of lanes, and number of lanes will have significant effects on the level of service and safety performance of the weaving sections. Eight years (2010-2017) of crash data in the type-A weaving sections was used in this analysis. The objective of this study aims to evaluate geometric design factors and operational factors on total crashes and each of the four crash types: rear-end, sideswipe, angle, and single-vehicle in type-A weaving sections using traditional negative binomial approach and develop crash modification factors (CMFs) to improve safety in the type-A weaving section. The results revealed that on-ramp traffic per hour, off-ramp traffic per hour, non-weaving traffic per hour, weaving ratio, length of the weaving section, direction of the freeway, width of inside shoulder, and width of outside shoulder were influencing crashes in type-A weaving sections. Furthermore, the estimated crash modification factors (CMFs) result revealed that total crashes gradually decrease as inside shoulder width increases. This implies that widening inside shoulder width have positive effects on weaving section safety. In addition, ramp metering, and advisory warning signs could improve safety in type-A weaving sections.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".