Driver distraction in school zones: A roadside observational study in Canada
Bibliographic record
Abstract
Distracted and impatient driving are top contributors to road crashes and fatalities. However, we lack sufficient data on the prevalence of dangerous behaviours and quantities of active transportation users integrated with motorized traffic in school zones – urbanized areas that exhibit higher rates of vulnerable road users. This study fills this gap by investigating the prevalence of driver distraction and impatient driving at seven locations in school zones in the city of Windsor, Ontario in Canada. Roadside observations were conducted during the Fall of 2024 and Winter of 2025 at peak activity periods during both the morning drop-off and afternoon pick-up times. Dangerous behaviours were measured as a factor of environmental factors (weather, season), time of the day (AM vs PM), and vehicle characteristics. Results showed an increased presence of dangerous driving in warmer months, with no significant differences being found between morning and afternoon. Approximately 20% of all drivers were engaged in distracting or impatient driving, with one in ten drivers being engaged in unlawful behaviours involving the use of handheld devices while driving. Drivers of larger vehicles also exhibited more dangerous driving. Our study adds to the distracted and impatient driving literature, and offers valuable information for road safety practitioners and regulators that can be used to implement more targeted road safety solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".