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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".