Article(id=1308462969067692054, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462963522823142, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20250129, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1753027200000, revisedDateStr=2025-07-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891481891, onlineDateStr=2026-09-20, pubDate=1784044800000, pubDateStr=2026-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891481891, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891481891, creator=13701087609, updateTime=1789891481891, updator=13701087609, issue=Issue{id=1308462963522823142, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='7', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1784044800000', pubDateStr='2026-07-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891480570, creator='13701087609', updateTime=1789892910062, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468959309751193, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462963522823142, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468959309751194, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462963522823142, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=29, endPage=35, ext={EN=ArticleExt(id=1308462969235464215, articleId=1308462969067692054, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=The Combination of HFACS and Parallel Apriori Algorithm in the Large-Scale Causal Analysis Application of Traffic Accident Data, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In the analysis of large-scale traffic accident data, traditional statistical methods suffer from low computational efficiency and limited ability to identify causes, making it difficult to effectively uncover deep-seated associations behind accidents. To address this issue, this paper proposes a traffic accident cause analysis method based on the Human Factors Analysis and Classification System (HFACS) framework combined with parallel Apriori algorithms. The method is developed from five aspects, including feature engineering, HFACS framework mapping and transactional structure transformation. Experimental results show that this approach significantly enhances the efficiency and accuracy of cause analysis in large-scale traffic accident data compared to traditional statistical analysis methods, providing more scientific data support for traffic accident prevention and safety management.

, authors=Yang Shao, Yangshan Tang, Hao He, authorsList=Yang Shao, Yangshan Tang, Hao He, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308462969440985112, articleId=1308462969067692054, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=人因分析与分类系统与并行Apriori算法结合在大规模交通事故数据致因分析中的应用, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对传统统计方法在大规模交通事故数据分析中计算效率低、致因识别能力有限的问题，从特征工程、人因分析与分类系统（HFACS）框架映射、事务型结构转换等5个方面提出了一种基于HFACS框架与并行Apriori算法结合的交通事故致因分析方法。验证结果表明，与传统统计分析方法相比，所提出的方法可有效提升大规模交通事故数据致因分析的效率和准确性，能够为交通事故预防与安全管理提供更加科学的数据支持。

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