Modeling lateral interactions between motorized vehicles and non-motorized vehicles in mixed traffic using accelerated failure duration model
Bibliographic record
Abstract
The objective of this study is to model the lateral interactions between motorized vehicles (MVs) and non-motorized vehicles (NMVs) in mixed traffic. Road user trajectories from two locations in China are extracted using computer vision techniques. The critical lateral distance (the shortest lateral distance to initiate avoidance maneuvers) is used as the lateral interaction indicator. Lateral interactions are modelled using the parametric accelerated failure time (AFT) duration model with a Weibull distribution, and the unobserved heterogeneity is considered using gamma frailty. The results show that interaction probabilities increase at higher MV speeds or NMV-MV speed differences and decrease with the NMV or MV yaw rates. The critical lateral distances when NMV ride in the MV lanes are shorter than those in the NMV lane. Moreover, bikes have higher interaction probabilities than e-bikes. These findings give insights into lateral interaction behaviours in mixed traffic and support better designs of such facilities.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".