Modeling and Analysis of Merging Behavior at Expressway On-Ramp Bottlenecks
Bibliographic record
Abstract
Merging behavior is inevitable for drivers at on-ramp bottlenecks and a significant factor in triggering a traffic breakdown. Empirical data were collected by extracting trajectories from merging vehicles and adjacent vehicles at two on-ramp bottlenecks in Shanghai, China. These data included 58 normal (free-flow) lane changes (NLCs), 36 cooperative lane changes (CLCs), 135 forced lane changes (FLCs), and 188 unsuccessful lane changes (USLCs). The objective was to develop and compare five discrete choice models (two multinomial logit and three nested logit) to understand merging behavior at on-ramp bottlenecks better. Estimation results showed that the two-level nested logit model considering three merging types (NLC, CLC, and FLC) provided the best fit. The traffic flow condition (bottleneck), the time gap and the space gap of the lag vehicle, and the speed of the merging vehicle were key factors when choosing merging types. The resulting quantitative models can be used to perform a microscopic analysis of the breakdown mechanism and develop a traffic simulation model.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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".