Big Data Analysis of Canadian Drivers Using OBDII Sensor Data: The Impact of the Pandemic
Bibliographic record
Abstract
Techniques to quantify driving skills and driving habits (e.g. duration and frequency of driving) are critical to the analysis of driving safety. Metrics related to driving are key to the analyses of safety topics such as differences in driving throughout the lifespan. An extensive literature examines such factors as novice/expert differences or the declines in driving ability that may occur with cognitive aging. Big Data analysis techniques can now be applied to the datasets being captured by automakers and insurance companies through in-car recording sensors to better understand changes in driving activity and patterns. These datasets include very large samples of drivers of all ages enabling the comparative study of all drivers including comparison of novice, and older driver groups. This paper leverages one such dataset to examine the driving patterns of Canadian drivers over one year while COVID 19 pandemic precautions were in effect. Specifically, one year of data for 7677 vehicles in Canada that have a single insured driver is examined. The dataset was captured during the pandemic and a general reduction in driving associated with pandemic precautions was observed. The report suggests that for several age groups, reductions in driving were larger for males than females.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".