Testing Daganzo’s Behavioral Theory for Multi-lane Freeway Traffic
Bibliographic record
Abstract
This report describes the detailed, albeit still preliminary study of traffic on stretches of two different freeways. Both were plagued by merge bottlenecks. The first of these sites is the Gardiner Expressway, a 3.3 km long freeway stretch in Toronto, Canada. The site was selected because of its suitable geometry (i.e. its merge bottleneck) and its well-tuned loop detectors located upstream and downstream of the bottleneck. The site thus provided for an exceptionally good “laboratory” for testing Daganzo’s behavior theory of drivers (Daganzo, 1999). It turns out that the observations from this stretch qualitatively match the theory in a number of important ways, as will be described in this report. The second site is a 1.8 km stretch of westbound Interstate 24 just upstream of the Caldecott Tunnel in Berkeley, California. This site provided a means for verifying Daganzo’s theory for “California conditions.” It is especially suitable for this study thanks to its very disruptive bottleneck and to its numerous vantage points (i.e., adjacent hillsides) from which to videotape traffic. Four cameras were strategically deployed along this freeway stretch. The detailed traffic data (manually) extracted from these videos were, like the Toronto data, found to be qualitatively consistent with much of Daganzo’s theory.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".