Article(id=1282032346677359167, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.09.1511, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744387200000, receivedDateStr=2025-04-12, revisedDate=1752076800000, revisedDateStr=2025-07-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589930455, onlineDateStr=2026-07-09, pubDate=1758988800000, pubDateStr=2025-09-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589930455, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589930455, creator=13701087609, updateTime=1783589930455, updator=13701087609, issue=Issue{id=1282032292440805784, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='9', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1758988800000', pubDateStr='2025-09-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589917526, creator='13701087609', updateTime=1783593837820, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048735404344166, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048735404344167, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=28, endPage=35, ext={EN=ArticleExt(id=1282032351312065088, articleId=1282032346677359167, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Developing an assessment criteria and method for intrinsic safety of smart highway system, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

This study developed a method for identifying intrinsic safety evaluation indicators and the associated assessment methods based on the LDA topic model. Firstly, the definition of intrinsic safety for smart highway was proposed. Guidelines for smart highway construction from 15 provinces and municipalities were collected. The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and the LDA topic model were applied to extract topics and keywords from the corpus. By integrating relevant standards and expert evaluation, an intrinsic safety performance evaluation index system for smart highway was established. This index system included four first-level indicators: safety perception and monitoring capability, traffic operation safety assurance capability, safety decision support capability, and system and equipment reliability, which are further divided into 15 second-level indicators. The connotation of each second-level indicator was described. Subsequently, the interactive relationships among the evaluation indicators of the intrinsic safety performance of smart highway were calculated using the ANP, and the weights of each evaluation indicator were determined using the Super Decision software. The results show that data transmission reliability, electromechanical equipment operation reliability, safety risk prediction and assessment capability are key indicators affecting the intrinsic safety level of smart highways. The developed evaluation methodology enables efficient extraction of valid data from textual sources, thereby enhancing the objectivity and accuracy of intrinsic safety assessment. This approach provides actionable guidance for advancing intrinsic safety development in smart highway.

, authors=Patrick X W ZOU^{1, 2}, Kunhao LIANG^{**, 1, 2}, authorsList=Patrick X W ZOU, Kunhao LIANG, authorCompany=null, correspAuthors=Kunhao LIANG, 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=1282032359679701571, articleId=1282032346677359167, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=智慧高速公路系统本质安全化评价指标体系, columnId=1149733271296193071, journalTitle=中国安全科学学报, columnName=安全社会科学与安全管理, runingTitle=null, highlight=null, articleAbstract=

为提升智慧高速公路本质安全水平，提出一种基于潜在狄利克雷分布（LDA）主题模型的智慧高速公路本质安全评价指标识别方法。界定智慧高速公路本质安全内涵，基于我国15个省市智慧高速公路建设指南文本，应用词频-逆文档频率（TF-IDF）算法和LDA主题模型提取语料主题及关键词，结合相关文献规范及专家访谈和评价，构建智慧高速公路本质安全评价指标体系，该体系涵盖安全感知与监测能力、交通运行安全保障能力、安全决策支持能力、系统与设备可靠性4个一级指标及15个二级指标，并阐释各二级指标内涵；应用网络层次分析法（ANP）揭示智慧高速公路本质安全评价指标间的交互关系，运用Super Decision软件确定各级评价指标的权重。研究结果表明：数据传输可靠性、机电设备运行可靠性、安全风险预测与评估能力等指标对智慧高速公路本质安全水平的影响最为显著。所构建的方法能够高效地从文本中挖掘出有效数据信息，提升本质安全评价的客观性与准确性，从而为智慧高速公路本质安全设计与管理提供指导。

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ZOU Patrick X W（1965—），男，广东紫金人，博士，特聘教授，博士生导师，主要从事复杂工程安全、风险和节能管理方面的研究与教学工作。E-mail: Patrick_Zou@chd.edu.cn。

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ZOU Patrick X W（1965—），男，广东紫金人，博士，特聘教授，博士生导师，主要从事复杂工程安全、风险和节能管理方面的研究与教学工作。E-mail: Patrick_Zou@chd.edu.cn。

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