Article(id=1314923091654967762, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1754150400000, receivedDateStr=2025-08-03, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791431695140, onlineDateStr=2026-10-08, pubDate=1773936000000, pubDateStr=2026-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791431695140, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791431695140, creator=13701087609, updateTime=1791431695140, updator=13701087609, issue=Issue{id=1314922986466009203, tenantId=1146029695717560320, journalId=1234093305789726721, year='2026', volume='46', issue='3', pageStart='1185', pageEnd='1776', issueExtLink='null', onlineDate='null', pubDate='1773936000000', pubDateStr='2026-03-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1791431670061, creator='13701087609', updateTime=1791431955369, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314924183251935526, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314924183251935527, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1725, endPage=1735, ext={EN=ArticleExt(id=1314923091998900691, articleId=1314923091654967762, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Research on carbon emission prediction of transportation industry based on MVMD decomposition, columnId=1234106390126588740, journalTitle=China Environmental Science, columnName=Carbon Emission Control, runingTitle=null, highlight=null, articleAbstract=

To address the issue of volatility and nonlinearity in carbon emission data sequences from the transportation sector affecting prediction accuracy, this study proposes a carbon emission prediction model that integrates Multivariate Variational Mode Decomposition (MVMD), differential multi-head attention mechanism, improved iTransformer, and LSTM enhanced by Sparrow Search Algorithm(SSA). First, MVMD was introduced to decompose the transportation carbon emission feature data sequence into modal components of different frequencies. Sample entropy was then used to quantify the complexity of each component, which was categorized into high- and low-frequency data based on entropy values, thereby further mitigating the volatility and nonlinearity of the carbon emission data sequence. Subsequently, SSA-LSTM was employed to predict low-frequency data, capturing long-range dependencies, while LSTM combined with the improved iTransformer was applied to rapidly denoise and fit high-frequency data to avoid interference with the primary trend. Finally, the prediction results of high- and low-frequency data were combined using weights calculated based on the sizes of sample entropy values. The model was validated using carbon emission data from China's transportation sector from 2000 to 2022. The results showed that the proposed model achieved a root mean square error (RMSE), mean square error (MSE), and mean absolute percentage error (MAPE) of 10.31×10⁶t, 106.35×10⁶t, and 0.97%, respectively, outperforming other comparative models and confirming its effectiveness.

, authors=Qing-rong WANG¹, Xin-kang LIU^{1, *}, Chang-feng ZHU², Jun-jie WANG¹, authorsList=Qing-rong WANG, Xin-kang LIU, Chang-feng ZHU, Jun-jie WANG, authorCompany=null, correspAuthors=Xin-kang 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=1314923092208615892, articleId=1314923091654967762, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于MVMD分解的交通运输业碳排放预测, columnId=1234106391661704058, journalTitle=中国环境科学, columnName=碳排放控制, runingTitle=null, highlight=null, articleAbstract=

针对交通运输业碳排放数据序列的波动性和非线性影响预测精度的问题，提出了一种结合MVMD分解、差分多头注意力机制、改进的iTransformer和SSA改进LSTM网络的交通运输业碳排放预测模型．首先，引入多元变分模态分解，将交通碳排放特征数据序列分解为不同频率的模态分量，再利用样本熵对各分量复杂度进行量化，并根据大小划分为高低频数据，进一步弱化交通碳排放数据序列的波动性和非线性；然后，使用SSA-LSTM进行低频数据的预测，挖掘长程依赖；同时，使用LSTM加改进的iTransfoemer快速去噪拟合，避免对主体趋势的干扰；最后，通过样本熵值的大小来计算权重将高低频数据预测值进行结合．最后，采用中国交通运输业2000~2022年碳排放数据来对模型进行验证，结果表明，所提模型的均方根误差、均方误差、平均绝对百分比误差分别为10.31×10⁶t、106.35×10⁶t、0.97%，均优于其他对比模型，验证了所提模型的有效性．

, authors=王庆荣¹, 刘心康^{1, *}, 朱昌锋², 王俊杰¹, authorsList=王庆荣, 刘心康, 朱昌锋, 王俊杰, authorCompany=null, correspAuthors=刘心康, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=BUyIU1I0lVcQWhY+5N1lhw==, magXml=34NxW85nWDYtSh6tLHS+SQ==, pdfUrl=null, pdf=uy8IFiP72xmPJWVfolJ2zg==, pdfFileSize=2010152, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=6lKbItBECpqKsDGXOdasHQ==, mapNumber=null, fund=null)}, authors=[Author(id=1314923094276407772, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923091654967762, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=329046272@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314923094360293854, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923091654967762, authorId=1314923094276407772, language=EN, stringName=Qing-rong WANG, firstName=Qing-rong, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314923094439985631, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923091654967762, authorId=1314923094276407772, language=CN, stringName=王庆荣, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.兰州交通大学电子与信息工程学院，甘肃 兰州 730070, bio={"content":"

王庆荣（1977-），女，宁夏固原人，教授，硕士，主要研究方向为交通运输系统优化与分析．发表论文20余篇．329046272@qq.com.

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王庆荣（1977-），女，宁夏固原人，教授，硕士，主要研究方向为交通运输系统优化与分析．发表论文20余篇．329046272@qq.com.

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