The ROADS project: Road observational assessment of driving distractions
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».