Two-Level Nested Logit Model to Identify Traffic Flow Parameters Affecting Crash Occurrence on Freeway Ramps
Bibliographic record
Abstract
This study analyzes the traffic flow conditions that affect crash occurrence on freeway ramps by type (on- or off-ramps) and configurations (diamond, loop, etc.). The study used the 5-min traffic flow data before the crash obtained from loop detectors to identify the traffic conditions contributing to crashes on ramps. With 5 years of ramp crash data on the Interstate 4 freeway in Orlando, Florida, a two-level nested logit model was developed to estimate the probabilities of crash occurrence for different ramp types and configurations. In the comparison of two nest structures, the traffic flow parameters contributing to crash occurrence greatly differed between on-ramps and off-ramps. The results of the model estimation suggested that the main-line speeds immediately upstream and downstream of ramps and the volume on ramps were correlated to crash occurrence on ramps. It is recommended that these traffic flow parameters be monitored in real time to detect elevated risk in traffic conditions on ramps within on-line systems for freeway traffic management.
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How this classification was reachedexpand
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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".