Agheli Ali, Aghabayk Kayvan
School of Civil Engineering, College of Engineering, University of Tehran, Iran.
Accid Anal Prev. 2025 Mar;211:107896. doi: 10.1016/j.aap.2024.107896. Epub 2024 Dec 13.
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.
骑自行车的人是道路上最脆弱的使用者之一，越来越容易受到各种干扰因素的影响，包括在城市环境中骑行时使用手机和从事其他活动。了解并减轻这些干扰对骑车人安全的影响至关重要。尽管这个问题很重要，但干扰对自行车碰撞事故中受伤严重程度的影响尚未得到广泛研究。本研究分析了来自碰撞报告抽样系统（CRSS）数据库的四年美国碰撞数据（2019 - 2022年），采用了一种混合框架，该框架集成了基于CatBoost的SHAP算法和具有均值和方差异质性的随机参数二元logit模型（RPBL - HMV）。所提出的方法证实了骑车人分心在碰撞受伤严重程度中所起的重要作用。随后，分析确定了影响分心骑车人碰撞事故中严重受伤可能性的几个因素。涉及机动车前部的碰撞事故，发生在农村地区、双向道路上、较高速度限制下以及周末时，严重受伤的概率更高。相反，在T型交叉路口发生的碰撞事故、涉及机动车侧面或后部的事故、骑车人戴头盔的事故或在高峰时段发生的事故，与严重受伤的可能性降低有关。值得注意的是，交互效应揭示了细微的模式。例如，虽然穿越道路行为和高峰时段单独来看会降低严重碰撞的可能性，但它们同时出现时会增加此类事故发生的概率。研究结果表明，应采取有针对性的安全措施和政策干预，通过减轻与分心相关的风险来提高骑车人的安全性，并促进更安全的骑行环境。
