Article(id=1309639635115012896, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250384, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1758470400000, revisedDateStr=2025-09-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172020938, onlineDateStr=2026-09-23, pubDate=1779552000000, pubDateStr=2026-05-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172020938, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172020938, creator=13701087609, updateTime=1790172020938, updator=13701087609, issue=Issue{id=1309639624411152909, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='5', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1779552000000', pubDateStr='2026-05-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172018387, creator='13701087609', updateTime=1790232830701, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894689805922601, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894689805922602, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=32, endPage=39, ext={EN=ArticleExt(id=1309639636926952225, articleId=1309639635115012896, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Expressway Traffic State Estimation Based on Connected Vehicles, columnId=1309639624981578254, journalTitle=Automobile Technology, columnName=Special Topic on Full-Stack Environment Perception Technology for Intelligent Vehicles, runingTitle=null, highlight=null, articleAbstract=

To improve the spatial coverage and timeliness of expressway traffic state estimation, this paper proposes a joint state estimation method that integrates observational data with a traffic flow model based on the Kalman filter. Intelligent Connected Vehicle (ICV) probe data are used as observational inputs. Based on Cooperative Adaptive Cruise Control (CACC) and the Intelligent Driver Model (IDM), the motion behaviors of ICV and human-driven vehicles in mixed traffic flow are simulated, and traffic density is estimated using the Cell Transmission Model (CTM). Validation using SUMO simulations and NGSIM field data shows that the estimation error of the proposed method decreases significantly as the ICV penetration rate increases. Specifically, the Mean Absolute Percentage Error (MAPE) is below 20% at a 20% penetration rate; the estimation accuracy improves further at 40%; and when the penetration rate reaches 80%, the MAPE is reduced by 16.54% compared with statistical estimation methods. The proposed method achieves high estimation accuracy and robustness, which is suitable for traffic state estimation in complex traffic environments.

, authors=Tao Guo¹, Pengpeng Sun¹, Jianqiu Wang², Zhendong Zhang¹, authorsList=Tao Guo, Pengpeng Sun, Jianqiu Wang, Zhendong Zhang, authorCompany=null, correspAuthors=Pengpeng Sun, 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=1309639637178610466, articleId=1309639635115012896, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于网联车的高速公路交通状态估计, columnId=1309639625430364898, journalTitle=汽车技术, columnName=智能汽车全栈环境感知技术专题, runingTitle=null, highlight=null, articleAbstract=

为了提升高速公路交通状态估计的空间覆盖能力与时效性，提出了一种基于卡尔曼滤波的观测信息与交通流模型的联合状态估计方法。以智能网联汽车（ICV）探测数据为观测输入，基于协同自适应巡航控制（CACC）与智能驾驶模型（IDM），对混合交通流中智能网联汽车与人类驾驶车辆（HDV）运动行为进行仿真，并结合元胞传输模型（CTM）完成交通流密度估计。SUMO仿真与NGSIM实测数据验证表明：所提出的模型对高速公路交通状态的估计误差随ICV渗透率的提升显著降低，在20%渗透率下，平均绝对百分比误差（MAPE）低于20%，渗透率提升至40%时，估计精度进一步提升，当渗透率提升至80%时，MAPE较统计估计方法降低16.54%，表明该模型具备较强的估计精度与稳定性，可适用于复杂交通环境。

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