区域因素对自行车损伤严重程度的异质性影响:潜在类聚类分析和部分比例优势模型

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
S. Wang, Jingfeng Ma, Hongliang Ding, Yuhuan Lu
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引用次数: 2

摘要

尽管骑自行车的好处被广泛接受,但由于骑自行车的人在道路上的脆弱性,自行车的安全性,特别是严重伤害,越来越受到关注。在文献中已经确定了导致不同自行车损伤严重程度的因素。对于地域性因素,考虑了与社会人口统计学和家庭特征、建筑环境、土地利用和交通状况相关的变量。然而,同时考虑自行车损伤严重程度的异质性和层次性的研究却很少。本研究利用伦敦低层超级输出区3年的碰撞数据集,研究了区域因素对不同自行车损伤严重程度的异质性影响,从而为文献做出了贡献。开发了潜在类聚类和部分比例几率相结合的方法。首先,基于潜在类聚类方法,对自行车碰撞事故的5个亚组进行识别;然后,针对不同的集群分别建立了部分比例模型。结果表明,一系列因素与自行车严重损伤的发生有关。然而,这些因素的影响在不同的集群中可能是不同的。例如,有些因素仅在特定的碰撞集群中有显著影响。此外,在一个或不同的集群中发现了相同因素的异质效应。研究结果可为自行车基础设施建设、交通管理和安全教育的发展提供参考,以提高骑自行车者的风险意识,减少严重自行车伤害的发生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the heterogeneous effects of zonal factors on bicycle injury severity: latent class clustering analysis and partial proportional odds models
Abstract Despite the benefits of cycling being widely accepted, bicycle safety—especially severe injury—has received increasing attention due to the vulnerability of bicyclists on the road. Factors contributing to varying bicycle injury severity have been identified in the literature. For the zonal factors, variables related to sociodemographic and household characteristics, built environments, land use, and traffic conditions are considered. However, it is rare that the heterogeneity and hierarchal features of bicycle injury severity are simultaneously considered. This study contributes to the literature by investigating the heterogeneous effects of zonal factors on varying bicycle injury severity, using a 3-year crash data set from the Lower Layer Super Output Areas of London. A combination of latent class clustering and partial proportional odds methods was developed. First, five subgroups of bicycle crashes were identified based on the latent class clustering method. Afterward, partial proportional models were developed separately for different clusters. Results indicate that a series of factors is found to be associated with the occurrence of severe bicycle injuries. However, effects of these factors could be distinctive among different clusters. For example, some factors only have significant impacts in the specific crash clusters. Furthermore, heterogeneous effects of the same factors in one or different clusters are discovered. The findings of this study can be helpful for the development of cycle infrastructures, traffic management, and safety education that can enhance the risk perception of bicyclists and reduce the occurrence of severe bicycle injuries.
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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