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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".