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首页> 外文期刊>Journal of Safety Research >Analysis of motorcycle accidents using association rule mining-based framework with parameter optimization and GIS technology
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Analysis of motorcycle accidents using association rule mining-based framework with parameter optimization and GIS technology

机译：基于参数优化和GIS技术的关联规则挖掘框架分析摩托车事故

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Introduction: 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. Method: 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. Results and conclusions: 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. (C) 2020 National Safety Council and Elsevier Ltd. All rights reserved.
机译：简介：分析摩托车事故的关键因素是减少死亡率的有效方法，提高道路安全性。关联规则挖掘（ARM）是一种有效的数据挖掘方法，以识别与伤害严重程度相关的关键因素。然而，现有的研究在施加臂上有一些限制：（a）大多数研究最主要的手臂参数阈值，这缺乏客观性和效率; （b）大多数研究仅列出了具有高参数阈值的规则，同时缺乏对多项规则的深入分析。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。方法：为了解决这些限制，本研究提出了一种基于ARM的框架，以确定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。提出了一种因子提取方法，以确定来自2项规则的单个关键因素，并从多项规则中提升因素。采用地理信息系统（GIS）探讨关键因素与摩托车损伤严重程度之间的空间关系。结果与结论：框架应用于澳大利亚维多利亚摩托车事故的案例研究。数据预处理后选择了十五个属性。 0.03和0.7被确定为臂上的最佳阈值和臂力的置信度。鉴定了五种个人关键因素和四个升压因素与致命伤害有关。空间分析由GIS进行，以呈现摩托车事故的热点。拟议的框架已被验证，以便在ARM中具有更好的参数优化和规则分析。实际应用：与致命因素相关的摩托车事故的热点在GIS地图中呈现。决策时，政策制定者可以直接参考这些地图。该框架可以应用于各种交通事故，以提高严重性分析的性能。 （c）2020国家安全委员会和elestvier有限公司保留所有权利。

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