Highway Traffic Accident Influence Area Based on Vehicle Bypass Decision-Making Index
Bibliographic record
Abstract
In order to reduce the impact of highway traffic accidents on surrounding road networks, accident influence area should be determined reasonably. According to the relationship between vehicle bypass decision-making index and threshold under accident condition, the vehicles’ route choice behavior at upstream of the accident spot can be divided into two types: bypass and nonbypass. Under nonbypass condition, the method of using traffic wave theory was put forward to determine the upstream influence area. Under bypass condition, the total number of bypass vehicles is determined based on bypass decision-making index being greater than bypass threshold. Assignment algorithms based on routes were proposed. Using improved Logit model to get the selection probability and the traffic flow of each route, then traffic flow of surrounding links could be obtained. At last, the road network influenced by the accident could be determined by comparing with the level of service of each link under normal condition. The paper takes Beijing-Kunming highway as an example, and the results show that the road network formed by the influenced links was highway traffic accident influence area. Comparing with the actual survey results, correctness of the calculation method is verified. Therefore, the analytical method based on bypass decision-making is applicable to determining highway accident influence area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".