Investigating and modeling crash risk for interactions between motorized and non-motorized in intersection center areas
Bibliographic record
Abstract
OBJECTIVE: Motorized vehicles (MV) and non-motorized vehicles (NMV) are mixed in the intersection center area (ICA). This mixing leads to complicated interactions between vehicles, which seriously affects traffic safety, especially at mixed intersections of high density. To deep understanding of the interaction course between motorized and non-motorized vehicles in ICAs. METHODS: Two intersections with a high density of interaction behavior between motorized and non-motorized vehicles were investigated through high-resolution traffic video. Firstly, to extract high-precision trajectories from roadside video, we proposed a new trajectory extraction framework that integrates Yolov7, Deepsort, and the trajectory reconstruction algorithm, which integrated the social force model and particle filtering (SFPF) proposed in our previous research. Second, 183 complete interaction events between motorized and non-motorized vehicles were extracted based on the surrogate safety indicator TTC, and latent variables affecting the course of interaction behavior between motorized and non-motorized vehicles were defined based on turning direction, kinetic state, surrounding environment, signal light, vehicle action behavior, and types of NMV. Third, an ordered logit model was built to study the interactions. RESULTS: or lower): the turning directions of the two vehicles, their speeds, steering behaviors, the distance between the conflict point and the vehicle, and the surrounding environment. The vehicles entering the ICA 10 s before the end of the signal phase have a higher probability of having a serious crash event while making the interaction. CONCLUSIONS: The study contributes to developing active safety control and driver assistance strategies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".