Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China.
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China.
J Safety Res. 2020 Dec;75:292-309. doi: 10.1016/j.jsr.2020.09.004. Epub 2020 Sep 26.
Analyzing key factors of motorcycle accidents is an effective method to reduce fatalities and improve road safety. Association Rule Mining (ARM) is an efficient data mining method to identify critical factors associated with injury severity. However, the existing studies have some limitations in applying ARM: (a) Most studies determined parameter thresholds of ARM subjectively, which lacks objectiveness and efficiency; (b) Most studies only listed rules with high parameter thresholds, while lacking in-depth analysis of multiple-item rules. Besides, the existing studies seldom conducted a spatial analysis of motorcycle accidents, which can provide intuitive suggestions for policymakers.
To address these limitations, this study proposes an ARM-based framework to identify critical factors related to motorcycle injury severity. A method for parameter optimization is proposed to objectively determine parameter thresholds in ARM. A method of factor extraction is proposed to identify individual key factors from 2-item rules and boosting factors from multiple-item rules. Geographic information system (GIS) is adopted to explore the spatial relationship between key factors and motorcycle injury severity.
The framework is applied to a case study of motorcycle accidents in Victoria, Australia. Fifteen attributes are selected after data preprocessing. 0.03 and 0.7 are determined as the best thresholds of support and confidence in ARM. Five individual key factors and four boosting factors are identified to be related to fatal injury. Spatial analysis is conducted by GIS to present hot spots of motorcycle accidents. The proposed framework has been validated to have better performance on parameter optimization and rule analysis in ARM. Practical applications: The hot spots of motorcycle accidents related to fatal factors are presented in GIS maps. Policymakers can refer to those maps straightforwardly when decision making. This framework can be applied to various kinds of traffic accidents to improve the performance of severity analysis.
分析摩托车事故的关键因素是降低死亡率和提高道路安全的有效方法。关联规则挖掘（ARM）是一种识别与伤害严重程度相关的关键因素的有效数据挖掘方法。然而，现有研究在应用 ARM 时存在一些局限性：（a）大多数研究主观地确定 ARM 的参数阈值，缺乏客观性和效率；（b）大多数研究只列出了高参数阈值的规则，而缺乏对多项目规则的深入分析。此外，现有的研究很少对摩托车事故进行空间分析，这可以为政策制定者提供直观的建议。
为了解决这些局限性，本研究提出了一种基于 ARM 的框架，以识别与摩托车伤害严重程度相关的关键因素。提出了一种参数优化方法，以客观地确定 ARM 中的参数阈值。提出了一种从二项规则中识别单个关键因素和从多项规则中识别增强因素的因素提取方法。采用地理信息系统（GIS）来探索关键因素与摩托车伤害严重程度之间的空间关系。
该框架应用于澳大利亚维多利亚州的摩托车事故案例研究。数据预处理后选择了十五个属性。确定 0.03 和 0.7 作为 ARM 中支持度和置信度的最佳阈值。确定了五个单个关键因素和四个增强因素与致命伤有关。通过 GIS 进行空间分析，呈现摩托车事故的热点。验证了该框架在 ARM 中的参数优化和规则分析方面具有更好的性能。实际应用：通过 GIS 地图呈现与致命因素相关的摩托车事故热点。政策制定者可以在决策时直接参考这些地图。该框架可应用于各种交通事故，以提高严重程度分析的性能。
