Analysis of Factors Affecting the Over-Representation of Sequential Crashes in Freeway Tunnels: Using Rule-Based Data Mining Method
Bibliographic record
Abstract
The paper provides an empirical analysis of road/tunnel design, traffic volume, and environmental factors associated with the increased likelihood of sequential crashes in freeway tunnels. The association rule mining and decision tree methods are employed since both of them are capable of identifying complicated interactions among variables and expressing them in the form of rules. Results show that tunnel length, traffic congestion, time of day, season, and vehicle type are the significant factors influencing the likelihood of sequential crashes in freeway tunnels. More importantly, association rule mining and decision tree analysis reveal that a combination of road/tunnel design, traffic, and environmental factors produces even a higher likelihood of sequential crashes, leading to a series of hazardous situations. For example, when factors including long tunnel and grade ≤ 2%, fourth level, and winter are combined, the proportion of sequential crashes is more than twice the average proportion of sequential crashes in the complete tunnel crash database. Traffic safety management should pay more attention to monitoring these hazardous situations which are more likely to be linked to sequential crashes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".