Risk-Taking Behavior of Left-Turners in Gap Acceptance and Its Effects on Capacity Estimation at Signalized Intersections
Bibliographic record
Abstract
The capacity of signalized intersections with a permissive left-turn phase is influenced significantly by the gap acceptance of left-turners. Left-turners' perception of risk when they are deciding whether to accept a gap is of great importance for understanding such driver behavior and for developing countermeasures to improve intersections. This study's objective was thus to model the tradeoff relationship between the perceived risk and the time-saving benefit in gap acceptance for left-turners in China. In the proposed model, the perceived risk was measured by the postencroachment time of the left-turning vehicle and of the gap-ending through vehicle at the conflict; the time-saving benefit was indicated by the potential time to wait for the next acceptable gap. A gap acceptance model that incorporated both variables was then developed and validated by using data collected at two intersections in Shanghai. The acceptable risk level of left-turners could then be defined as the ratio of the estimated model coefficients of those two variables. Results indicated that the wait time significantly affected the gap acceptance and the critical gap decreased as the acceptable risk level rose. In addition, the acceptable risk level was found to be approximately 60 at the observed intersections. With the results, impacts of the acceptable risk level on the capacity of permissive left-turn traffic were investigated through a numerical study. The findings revealed that the capacity of permissive left-turn traffic could be stochastic in nature rather than constant because of random traffic flow characteristics as well as different risk perceptions of drivers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".