The ROADS project: Road observational assessment of driving distractions
Bibliographic record
Abstract
BACKGROUND: Globally, motor-vehicle collisions cause 1.35 million deaths and more than 78 million injuries every year, with distracted driving contributing to many of these tragedies. Our main objective was to covertly determine the proportion of distracted drivers in live traffic. METHODS: ROADS was a covert observational study conducted from November 2020-June 2021. We observed drivers on the highways and urban streets between Hamilton and Toronto, Ontario. The research team observed drivers of moving vehicles and collected data covertly while driving beside them in live traffic. Moving passenger vehicles ahead of the research team were randomly screened for inclusion. Stopped/parked vehicles, buses, and semi-trucks were excluded. Demographic and safety variables included estimated age and sex, seatbelt usage, and two-handed driving. Driving distractions were categorized as in-vehicle, outer-vehicle, and mobile phones. Driving errors, such as lane drift, evasive maneuvers, and near-crash/crash, were recorded. We analyzed associations between demographic and situational variables (weekday/weekend, urban/highway, presence/absence of passenger) and distracted driving, as well as associations between driving errors and distracted driving. RESULTS: Of the observed 1,105 drivers, 609 (55.1%) were distracted. In-vehicle distractions (42.3%, 467/1105) were most prevalent, while 151 (13.7%) drivers were using mobile phones. Hands-free usage was observed in 92 (8.3%) drivers, while 63 (5.7%) drivers used a handheld device, visibly manipulating (3.4%, 38/1105), or actively talking (2.3%, 25/1105). Of the 24 (2.2%) drivers observed exhibiting driving errors, 23 (95.8%) drivers were visibly distracted. Younger estimated age (under 30 years old: OR 2.0, CI 1.320-3.105; 30-50 years old: OR 1.5, CI 1.090-1.925), and driver errors were significantly associated with distracted driving (p < 0.005). Sex, urban vs highways, and weekday vs weekend did not demonstrate a statistically significant association with distracted driving. CONCLUSION: By covertly observing moving vehicles while actively participating in live traffic, we identified that 55.1% of drivers were distracted, and approximately one in seven drivers used their mobile phones. Of the 24 drivers who were recorded making driving errors, an astounding 95.8% (23) were distracted, with two-thirds of these drivers illegally engaging with their phones. Also, driving on city streets versus highways (>60 km/hr) did not play a role in distracted driving. All this indicates that distracted driving is not only prevalent but also pervasive. Future research should focus on targeted driver education and behavioral modification. PRACTICAL APPLICATIONS: This data can be applied towards driver education programs counseling drivers on dangerous distracting behaviors, as well as influencing legislature, informing, and providing law enforcement insight into worrisome patterns of distracted driving.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".