How does distraction affect cyclists’ severe crashes? A hybrid CatBoost-SHAP and random parameters binary logit approach

•Analyzed four years (2019–2022) of U.S. CRSS data on distracted cyclist crashes.•A hybrid machine learning and advanced statistical modeling approach was employed.•Identified risk factors increasing severe injury probability for distracted cyclists.•Findings support targeted safety measures to miti...

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Vydané v:	Accident analysis and prevention Ročník 211; s. 107896
Hlavní autori:	Agheli, Ali, Aghabayk, Kayvan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Elsevier Ltd 01.03.2025
Predmet:	Accidents, Traffic - statistics & numerical data Adult Algorithms Bicycle-motor vehicle crash Bicycling - injuries Bicycling - psychology Boosting Machine Learning Algorithms Crash severity Databases, Factual Distracted cyclist Distracted Driving - statistics & numerical data Female Humans Interpretable ML Logistic Models Machine learning Male Middle Aged Risk Factors United States - epidemiology Unobserved heterogeneity Wounds and Injuries - epidemiology United States Interpretable ML Unobserved heterogeneity Machine learning Crash severity Bicycle-motor vehicle crash Distracted cyclist
ISSN:	0001-4575, 1879-2057, 1879-2057
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