Observations and Analysis of Multistep-Approach Lane Changes at Expressway Merge Bottlenecks in Shanghai, China
Bibliographic record
Abstract
One major cause of accidents at signalized intersections is vehicles running the red light. To discourage red light running, many authorities have installed red light cameras. From numerous field observations at two expressway merge bottlenecks, this paper identifies and studies a peculiar type of lane change, referred to as the multistep-approach lane change (MALC). The characteristics and detailed maneuvers of the MALC are first described and compared with three traditional types of lane changes (normal, cooperative, and forced). Next, descriptive parameters, such as the lane-changing duration and velocity and the number of affected vehicles, are investigated and analyzed during the transline ride (TLR) period. The parameters are taken from 132 sets of vehicle trajectory data collected at two merge bottlenecks in Shanghai, China. Significant differences are found between the MALC and traditional lane changes: the MALC takes longer to complete (10 s on average), involves lower lane-changing velocity (15 km/h on average during the TLR period), and affects more vehicles (six vehicles on average). As such, the MALC poses more disruptive influences on the traffic flow and could explain the occurrences of rapid drops in capacity at expressway merge bottlenecks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".