Rear-end conflicts analysis at non-signalized intersection based on vehicles trajectory data
Bibliographic record
Abstract
Abstract With the raise of implementation of both signalized and ITS intersections at many municipalities around the world, countries such as Germany, USA, Canada and others still use the stop-control (non-signalized) intersections in their traffic network systems. The safety of these non-signalized intersections has been a major concern for researchers and city planners. Therefore, this study aims to investigate the safety in terms of exploring the rear-end conflicts of non-signalized intersections in a Two-way stop intersection in Germany. The Intersection Drone Dataset from an intersection in the city of Aachen in Germany is used to measure traffic conflicts between car-following (leading and following vehicles) when approaching the intersection, then the microscopic variables leading to these conflicts are explored using the random parameter logit model with heterogeneity in means and variances. The results show that there is a concerning number of conflicts over a short period of time at the non-signalized intersection and variables such as the standard deviation velocity of the leading vehicle, the average acceleration of the leading vehicle, the average velocity of the following vehicle, the average acceleration of the following vehicle and the difference of distance between leading and following vehicles are found to be significant. In addition, a new phenomenon, Unnecessary Intended Deacceleration, of car-following events which increases the safety risk at the non-signalized intersection is briefly addressed. The findings of the study underscore the urgent need for proactive intervention strategies to reduce rear-end conflicts at non-signalized intersections.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".