Personal efficiency in highway driving: An agent-based model of driving behaviour from a system design viewpoint
Bibliographic record
Abstract
This research investigates improvement in personal efficiency of a driver based on the driver behaviour on a generic three-lane highway in Ontario. The behaviour is classified in: Law-Abiding drivers, opportunistic Deviant drivers, cautious Non-Myopic drivers, tailgating Aggressive drivers, and ambivalent Imitator drivers. For each driver in a class, the instantaneous ratio between actual and preferred speed is measured. A ratio of 1 defines an efficient driver. Here we measure throughout the personal efficiency of a driver by comparing their ratio to 1. We then aggregate these personal measures for classes of drivers; the system is more efficient if the aggregate ratio is closer to 1. Our research leads to the following conclusions: efficiency declines with higher car densities; it improves with the presence of Deviant and Aggressive drivers; Non-Myopic drivers do not affect traffic flow in a significant way. Imitators are Law-Abiding drivers who temporarily mimic deviating driver behaviour. We note that Imitator drivers fare worse than Law-Abiding drivers, but slightly improve overall system efficiency. The most surprising conclusion was to see that although Deviant drivers deviate from traffic rules from a selfish desire to improve their personal efficiency, they fare the worst in the end, while overall contributing to all other drivers' personal efficiencies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".