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A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

机译：基于原因分析的交通事故数据挖掘混合算法

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摘要

Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable.
机译：道路交通事故数据库为道路交通事故分析提供了基础，其中的数据通常具有放射状，多维和多层结构。传统数据挖掘算法（例如关联规则）单独应用时，通常会产生不确定和不可靠的结果。本文提出的一种改进的基于粒子群算法的关联规则算法可以用于分析事故属性与原因之间的相关性。该新算法着眼于道路交通事故数据超立体结构的特征，可以更准确，更高地计算事故原因的关联规则。还定义了关联熵的新概念，以帮助比较不同事故属性之间的重要性。运用T检验模型和Delphi方法对改进算法的准确性进行了检验和验证，其结果是平均随机交通事故数据采样分析的速度提高了十倍。在本文中，该算法在包含两万多个项目的样本数据库中进行了测试，每个项目具有56个事故属性。最终结果证明了改进算法的准确性和稳定性。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第1期|302627.1-302627.8|共8页
  • 作者

    Jianfeng Xi; Zhenhai Gao; Shifeng Niu; Tongqiang Ding; Guobao Ning;

  • 作者单位

    State Key Laboratory of Automobile Dynamic Simulation, Jilin University, Changchun 130022, China,College of Traffic, Jilin University, Changchun 130022, China;

    State Key Laboratory of Automobile Dynamic Simulation, Jilin University, Changchun 130022, China;

    School of Automobile, Chang'an University, Xi'an 710064, China;

    College of Traffic, Jilin University, Changchun 130022, China;

    School of Automotive Engineering, Tongji University, Shanghai 201804, China;

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