A system dynamics approach for assessing the impacts of autonomous vehicles on collision frequency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study employs a System Dynamics (SD) approach to assess the long-term impact of Autonomous Vehicles (AVs) on traffic collisions. By modeling key variables affecting collision frequency, the SD framework represents existing transportation systems and incorporates AV adoption to evaluate changes in collision rates. The models predict the frequency of Property Damage Only (PDO) and Fatal-Injury (FI) collisions at different levels of AV market penetration. Two policy scenarios are examined: incentives for shared AVs and higher occupancy rates for shared AV services. Results indicate significant improvements in traffic safety as AV penetration increases, particularly when coupled with shared AV usage and advanced sensing and communication technologies. The models provide valuable insights into the complex interactions between AVs and collision-related factors. These findings support the development of effective policies to guide the adoption of AV technology, reduce accidents, and enhance overall road safety in the long term.
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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 it