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

• Published in: Accident analysis and prevention Vol. 211; p. 107896
• Main Authors: Agheli, Ali, Aghabayk, Kayvan
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.03.2025
• Subjects: 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
• DOI: 10.1016/j.aap.2024.107896
• ISSN: 0001-4575; 1879-2057

•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 mitigate distracted cyclist crash risks. Cyclists are among the most vulnerable road users, increasingly subject to various sources of distraction, including the use of mobile phones and engagement in other tasks while navigating urban environments. Understanding and mitigating the impact of these distractions on cyclist safety is crucial. Despite the importance of this issue, the effect of distraction on injury severity in cycling crashes has not been extensively studied. This research analyzes four years of U.S. crash data (2019–2022) from the Crash Report Sampling System (CRSS) database, employing a hybrid framework that integrates CatBoost-based SHAP algorithm and the random parameters binary logit model with heterogeneity in means and variances (RPBL-HMV). The proposed approach confirms the significant role of cyclist distraction in crash injury severity. Subsequently, the analysis identifies several factors influencing the likelihood of severe injuries in distracted cyclist crashes. Crashes involving the front of motor vehicles, occurring in rural areas, on two-way roads, at higher speed limits, and during weekends were associated with a higher probability of severe injuries. Conversely, crashes at T-intersections, involving the side or rear of motor vehicles, where cyclists wore helmets, or during rush hour were linked to a reduced likelihood of severe injuries. Notably, interaction effects reveal nuanced patterns. For instance, while crossing roadway actions and rush hour periods individually decrease the likelihood of severe crashes, their combination increases the probability of such outcomes. The findings suggest targeted safety measures and policy interventions aimed at enhancing cyclist safety and promoting safer cycling environments by mitigating distraction-related risks.