Chao Chun-Chieh, Ma Hon-Ping, Wei Li, Lin Yen-Nung, Chen Chenyi, Saleh Wafaa, Wiratama Bayu Satria, Widodo Akhmad Fajri, Hsu Shou-Chien, Ko Shih Yu, Lin Hui-An, Chan Cheng-Wei, Pai Chih-Wei
Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei City, Taiwan.
Department of Emergency Medicine, Taipei Medical University Hospital, Taipei City, Taiwan.
PLoS One. 2025 Jan 3;20(1):e0315692. doi: 10.1371/journal.pone.0315692. eCollection 2025.
Relevant research has provided valuable insights into risk factors for bicycle crashes at intersections. However, few studies have focused explicitly on three common types of bicycle crashes on road segments: overtaking, rear-end, and door crashes. This study aims to identify risk factors for overtaking, rear-end, and door crashes that occur on road segments.
We analysed British STATS19 accident records from 1991 to 2020. Using multivariate logistic regression models, we estimated adjusted odds ratios (AORs) with 95% confidence intervals (CIs) for multiple risk factors. The analysis included 127,637 bicycle crashes, categorised into 18,350 overtaking, 44,962 rear-end, 6,363 door, and 57,962 other crashes.
Significant risk factors for overtaking crashes included heavy goods vehicles (HGVs) as crash partners (AOR = 1.30, 95% CI 1.27-1.33), and elderly crash partners (AOR = 2.01, 95% CI = 1.94-2.09), and decreased risk in rural area with speed limits of 20-30 miles per hour (AOR = 0.45, 95% CI = 0.43-0.47). For rear-end crashes, noteworthy risk factors included unlit darkness (AOR = 1.49, 95% CI = 1.40-1.57) and midnight hours (AOR = 1.28, 95% CI = 1.21-1.40). Factors associated with door crashes included urban areas (AOR = 16.2, 95% CI = 13.5-19.4) and taxi or private hire cars (AOR = 1.61, 95% CI = 1.57-1.69). Our joint-effect analysis revealed additional interesting results; for example, there were elevated risks for overtaking crashes in rural areas with elderly drivers as crash partners (AOR = 2.93, 95% CI = 2.79-3.08) and with HGVs as crash partners (AOR = 2.62, 95% CI = 2.46-2.78).
The aforementioned risk factors remained largely unchanged since 2011, when we conducted our previous study. However, the present study concluded that the detrimental effects of certain variables became more pronounced in certain situations. For example, cyclists in rural settings exhibited an elevated risk of overtaking crashes involving HGVs as crash partners.
相关研究为交叉路口自行车碰撞事故的风险因素提供了有价值的见解。然而,很少有研究明确关注路段上三种常见的自行车碰撞类型:超车碰撞、追尾碰撞和车门碰撞。本研究旨在确定路段上发生的超车、追尾和车门碰撞的风险因素。
我们分析了1991年至2020年英国的STATS19事故记录。使用多变量逻辑回归模型,我们估计了多种风险因素的调整优势比(AOR)及95%置信区间(CI)。分析包括127,637起自行车碰撞事故,分为18,350起超车碰撞、44,962起追尾碰撞、6,363起车门碰撞和57,962起其他碰撞事故。
超车碰撞的显著风险因素包括重型货车(HGV)作为碰撞对象(AOR = 1.30,95% CI 1.27 - 1.33)、老年碰撞对象(AOR = 2.01,95% CI = 1.94 - 2.09),以及在限速为每小时20 - 30英里的农村地区风险降低(AOR = 0.45,95% CI = 0.43 - 0.47)。对于追尾碰撞,值得注意的风险因素包括无照明的黑暗环境(AOR = 1.49,95% CI = 1.40 - 1.57)和午夜时段(AOR = 1.28,95% CI = 1.21 - 1.40)。与车门碰撞相关的因素包括城市地区(AOR = 16.2,95% CI = 13.5 - 19.4)以及出租车或私人租车(AOR = 1.61,95% CI = 1.57 - 1.69)。我们的联合效应分析揭示了其他有趣的结果;例如,在农村地区,当老年司机作为碰撞对象(AOR = 2.93,95% CI = 2.79 - 3.08)以及HGV作为碰撞对象(AOR = 2.62,95% CI = 2.46 - 2.78)时,超车碰撞的风险会升高。
自我们之前进行研究的2011年以来,上述风险因素基本保持不变。然而,本研究得出结论,某些变量的不利影响在某些情况下变得更加明显。例如,在农村环境中,骑自行车者与HGV作为碰撞对象发生超车碰撞的风险升高。