Ulak M Baran, Asadi Mehrnaz, Geurs Karst T
Department of Civil Engineering and Management, University of Twente, Enschede 7522 NB, the Netherlands.
Department of Civil Engineering and Management, University of Twente, Enschede 7522 NB, the Netherlands; City of Amsterdam, Amsterdam 1093 NG, the Netherlands.
Accid Anal Prev. 2025 Mar;211:107872. doi: 10.1016/j.aap.2024.107872. Epub 2024 Dec 24.
In the Netherlands and all over the world, traffic safety problem has been growing particularly for cyclists over the last decades with more people shifting to cycling as a healthy and sustainable mode of transport. Literature shows that age is an important factor in crash involvement and consequences; however, few studies identify the risk factors for cyclists from across different age groups. Therefore, this study aims to identify and understand the effects of traffic, infrastructure, and land use factors on vehicle-to-bike injury and fatal crashes involving cyclists from different age groups. For this purpose, we adopted an approach consisting of resampling and machine learning (XGBoost-Tweedie) techniques to analyse police-reported crashes between the years 2015 and 2019 in the Netherlands. The analysis shows that effects of external variables on crashes widely vary among different age groups and the analysis of total crash rates may not disclose the nature of crashes of cyclist from different age groups. The analysis also shed light on the nonlinear effects of traffic and built environment factors on cyclist crashes, which are usually disregarded in the traffic safety literature. The proposed approach and findings provide a profound understanding of the nature of cyclist crashes and the complex relationships between factors, which can contribute to developing effective crash prevention strategies tailored to different age groups.
在荷兰乃至全世界,在过去几十年里,交通安全问题日益突出,尤其是对于骑自行车的人来说,因为越来越多的人转向将骑自行车作为一种健康且可持续的交通方式。文献表明,年龄是交通事故参与度和后果的一个重要因素;然而,很少有研究能确定不同年龄组骑自行车者的风险因素。因此,本研究旨在识别并了解交通、基础设施和土地利用因素对涉及不同年龄组骑自行车者的车辆与自行车碰撞致伤及致命事故的影响。为此,我们采用了一种由重采样和机器学习(XGBoost - Tweedie)技术组成的方法,来分析2015年至2019年荷兰警方报告的事故。分析表明,外部变量对事故的影响在不同年龄组之间差异很大,而对总事故率的分析可能无法揭示不同年龄组骑自行车者事故的本质。该分析还揭示了交通和建成环境因素对骑自行车者事故的非线性影响,而这在交通安全文献中通常被忽视。所提出的方法和研究结果为深入了解骑自行车者事故的本质以及各因素之间的复杂关系提供了帮助,这有助于制定针对不同年龄组的有效事故预防策略。