Zero-Collision Models for Urban Mobility: A Data-Driven Technological Framework
Bibliographic record
Abstract
The last few years there has been an effort to eradicate traffic collisions termed for North American cities under “Vision Zero.” It seems that removing infrastructure advancements and policies will always be crucial, but at some point in the future, data driven technology will dominate the urban mobility space. This paper presents a real time analytics driven collision mitigation system with focus on data from connected and autonomous vehicles alongside simulation platforms. We analyze literature that documents these claims, particularly how computation of large datasets, simulation engines, and safety rules for self-driving vehicles can significantly diminish or completely eliminate traffic collisions. Further, we demonstrate a proof of concept case of a city hackathon, stationed “VANquish Collisions,” in Vancouver, Canada, which can facilitate the development of working prototype tools. This paper claims that with the marriage of intelligent infrastructure, data pipelines, and automation in vehicles, cities can truly start to work towards actionably reducing collision incidents to zero.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".