Article(id=1281685554907549862, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281685530169545672, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.06.0642, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739980800000, receivedDateStr=2025-02-20, revisedDate=1745596800000, revisedDateStr=2025-04-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1783507248855, onlineDateStr=2026-07-08, pubDate=1751040000000, pubDateStr=2025-06-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783507248855, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783507248855, creator=13701087609, updateTime=1783507248855, updator=13701087609, issue=Issue{id=1281685530169545672, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='6', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1751040000000', pubDateStr='2025-06-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783507242958, creator='13701087609', updateTime=1783589309504, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282029742274622413, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281685530169545672, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282029742274622414, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281685530169545672, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=88, endPage=97, ext={EN=ArticleExt(id=1281685555096293543, articleId=1281685554907549862, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Research on risky driving behavior spectrum of tunnel entrance and exit based on trajectory data, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To identify the primary types and distribution patterns of risky driving behaviors at the entrance and exit of the highway tunnel, a risk driving behavior spectrum was constructed based on trajectory data. Firstly, trajectory data were collected through continuous video surveillance of Liupan Mountain Tunnel in Ningxia. Vehicle traffic characteristics at tunnel entrance and exit were analyzed and four categories of risky driving behaviors were selected, including rapid speed change, serpentine driving, dangerous following, and dangerous lane changing. Then, the measurement of risk method was applied to quantify four types of risky driving behaviors, and quartile deviation and criteria importance though intercriteria correlation (CRITIC) were used to determine the threshold values of risky driving behavior characteristics and weights, and the characteristic values of drivers' risky driving behavior spectrum were calculated. Finally, driving behavior scores were compared with characteristic thresholds, and the spatial distribution of hazard points for typical risky behaviors of dangerous drivers was statistically analyzed. The results of the study show that high-risk drivers in the entrance/exit sections of the tunnel mainly show the behaviors of rapid speed change or serpentine driving, in which the rapid speed change behavior has the most significant effect on the total score of the risky behavioral spectrum of the drivers. Vehicles have large speed variations at both the inside the tunnel entrance and outside the exit from 0 to 50 meters. Moreover, larger speed changes are observed at the exit section (0-50 m) compared to the entrance section, and risk points for rapid speed change behavior of dangerous drivers are also more concentrated in that segment. The lateral offset values outside the tunnel entrance/exit are larger than those inside the tunnel, and the risk points for serpentine driving behavior of dangerous drivers are mainly distributed in the range of 0-50 meters outside the tunnel entrance and exit. Risky driving behavior spectrum based on trajectory data can assess and quantify the risk level of drivers in the tunnel entrance/exit sections, facilitating precise identification of high-risk drivers.

, authors=Xingliang LIU¹, Jidong XU¹, Tangzhi LIU^{**, 1}, Xingjun WANG¹, Guoju TAN², authorsList=Xingliang LIU, Jidong XU, Tangzhi LIU, Xingjun WANG, Guoju TAN, authorCompany=null, correspAuthors=Tangzhi LIU, 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=1281685555633164458, articleId=1281685554907549862, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于轨迹数据的隧道进出口风险驾驶行为谱研究, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为明确隧道进出口路段主要风险驾驶行为种类及分布特征，基于卡口轨迹数据构建风险驾驶行为谱。首先，基于宁夏六盘山隧道连续视频监控采集轨迹数据，选取4类风险驾驶行为，即急变速、蛇形驾驶、危险跟驰和危险换道；然后，使用风险度量法计算风险驾驶行为特征参数，以四分位差法和CRITIC权重法确定风险驾驶行为特征参数阈值和权重，综合得到个体驾驶员风险驾驶行为谱特征值；最后，对比驾驶行为得分和特征阈值统计危险驾驶员的典型风险行为危险点空间分布。结果表明：隧道进出口段高风险驾驶员主要表现出急变速或蛇形驾驶行为，其中急变速行为对驾驶员风险行为谱总得分的影响最为显著；车辆在隧道进口洞内和出口洞外0~50 m均有较大速度变化，且与进口相比出口0~50 m速度变化幅度更大，危险驾驶员的急变速行为风险点在该区段分布也更为集中；隧道进出口的洞外横向偏移值均大于洞内，危险驾驶员的蛇形驾驶行为风险点则主要分布在隧道进出口的洞外0~50 m范围内；基于轨迹数据的风险驾驶行为谱可评估量化隧道进出口段驾驶员的风险程度，有助于精细化识别高风险驾驶人。

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刘星良（1989—），男，陕西西安人，博士，副教授，主要从事交通安全、交通流理论等方面的研究。E-mail: xingliang@cqjtu.edu.cn。

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刘星良（1989—），男，陕西西安人，博士，副教授，主要从事交通安全、交通流理论等方面的研究。E-mail: xingliang@cqjtu.edu.cn。

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