Risk Analysis of Vehicle Rear-End Collisions at Intersections
Bibliographic record
Abstract
Aiming at solving a typical problem of past research using accident experience statistics of being unable to adapt to changing traffic flows, this paper provides an evaluation method of the risk of vehicle rear-end collisions at red-light-camera (RLC) intersections based on theoretical probabilities. Taking advantage of trajectory data of vehicles at the two similar intersections, which are Cao’an Road and Lvyuan Road with RLCs and Cao’an Road and Anhong Road without RLCs in Shanghai, a binary logit (BL) model of stop-and-go decision-making is established. Using the model and adjusting the headway and potential travel time, we can perform simulation and analysis of rear-end collisions. The result shows that this method is feasible to analyse the influence of RLCs on rear-end collisions. The analysis indicates that RLCs can cause higher speeds for vehicles passing the RLC intersection and more abnormal driving behaviors, which increase the difficulty of stop-and-go decision-making. RLCs do not always lead to an increase of rear-end collisions. For vehicles close to or far from intersection at the decision-making time, RLCs will significantly reduce the possibility of rear-end collisions; however, for vehicles in the potential travel time of 2 s∼3 s, RLCs will increase the probability of rear-end collisions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".