Rear-end conflicts analysis at non-signalized intersection based on vehicles trajectory data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it