Empirics of Multianticipative Car-Following Behavior
Bibliographic record
Abstract
This paper considers multianticipative car-following behavior (i.e., driver behavior that includes responses to multiple vehicles ahead). Two well-known models incorporating multivehicle stimuli (Bexelius and Lenz) are recalled, and various modifications are proposed to improve their performance. With vehicle trajectories for a motorway collected from a helicopter and a newly developed approach to parameter identification, new empirical evidence of multianticipative car-following is provided with estimates of the driver-specific parameters of the considered multianticipative car-following models. In doing so, one can investigate the nature of multileader stimuli, including insights into the number of vehicles ahead to which drivers react and the kind of stimuli to which drivers respond. Large interdriver variability in multileader driving behavior is also presented. In the last part of the paper, implications of the research findings for microscopic modeling are discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".