Crossing conflict models for urban un-signalized T-intersections in India
Why this work is in the frame
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Bibliographic record
Abstract
Traffic conflict is frequently utilized as a stand-in for crashes for analyzing traffic safety from a broader perspective for varying roadways and traffic conditions. In Indian heterogeneous traffic conditions, vehicles with various static and dynamic properties interact simultaneously in longitudinal and lateral directions, forming traffic conflicts. To this end, the present study develops crossing conflict-based safety performance functions (C-SPFs) for eight urban un-signalized T-intersections. The video-graphic survey approach was used to gather the necessary traffic data with different intersection and traffic flow characteristics. After that, from the recorded video, traffic conflicts were identified using the Post encroachment time (PET) for the selected eight study intersections. Based on the PET values, crossing conflicts were initially divided into critical conflicts (CC) and non-critical conflicts (NCC). Then, using the Poisson-Tweedie regression technique, crossing conflicts were modeled as a function of traffic flow and intersection-related parameters. The findings showed that the most important factors defining the number of CC and NCC are intersection geometry (with or without Central Island), time of day, traffic volume, and composition (offending and conflicting approach). Based on the study’s findings, city planners and traffic engineers estimate the number of CC and NCC; as a result, they may project the necessary laws, rules, and regulations to enhance traffic safety operations.
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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 it