基于关联规则的交通事故影响因素的挖掘

摘要/Abstract

摘要： 道路交通安全是一个公共的安全问题,每年因交通事故死亡的人数在所有安全事故导致的总死亡人数中占比最高。随着大数据智能分析技术的发展,广泛利用交通数据朔源事故原因,有利于提出针对性措施,预防交通事故的发生。文中针对导致交通事故的原因具有多样性的特点,提出利用交通事故的相关新闻数据,广泛结合新闻报道具有的真实性和时效性特点来进行交通事故因素及责任的分析。以新浪网站交通事故新闻为数据源,从新闻事件中提取引发交通事故的相关因素。针对经典Apriori只适用于单一维度的关联挖掘以及需要频繁扫描数据库的缺点,提出了改进的多值属性Apriori算法。以省市为关注点,挖掘出导致事故发生的多种组合因素,由此总结出省市多发交通事故的规律,并提供给有关部门作为采取预防和监管措施的依据。

关键词: 多值属性关联规则, 交通事故, 数据库, 数据挖掘

Abstract: The road traffic safety is a public safety issue.The number of deaths due to traffic accidents account for the highest proportion in all accidents every year.With the development of big data intelligent analysis technology,the traffic accident data are extesively used to trace the causes,it is helpful to propose specific measures to avoid and prevent the occurrence of traffic accidents.According to the characteristics of diversity causes of traffic accidents,this paper proposd to use the news’ data of traffic accident combining with a wide range of news’ authenticity and characteristics timeliness to do the analysis of factors and the liability of traffic accidents.Taking the traffic accident news in Sina as the data source,the relevant factors of traffic accidents are extracted from it.In terms of the limitation in classic Apriori that only applies to a single dimension association mining and needs to scan database frequently,an improved multi-va-lued attribute Apriori algorithm was proposed.Focuing on the traffic accident data of provinces and cities,a variety of combination factors which lead to these traffic accidents were mined,thus the rules of frequent traffic accidents in pro-vinces and cities were summarized as the basis for taking preventive and regulatory measures.

Key words: Data mining, Database, Multi-valued attribute association rules, Traffic accident

中图分类号:

TP181

贾熹滨,叶颖婕,陈军成. 基于关联规则的交通事故影响因素的挖掘[J]. 计算机科学, 2018, 45(6A): 447-452. https://doi.org/

JIA Xi-bin,YE Ying-jie,CHEN Jun-cheng. Influence Factors Mining of Traffic Accidents Based on Association Rules[J]. Computer Science, 2018, 45(6A): 447-452. https://doi.org/

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