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To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good.<\/jats:p>","DOI":"10.1155\/2019\/9162163","type":"journal-article","created":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T23:34:22Z","timestamp":1559604862000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Multimode Dynamic Short\u2010Term Traffic Flow Grey Prediction Model of High\u2010Dimension Tensors"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3458-3905","authenticated-orcid":false,"given":"Huiming","family":"Duan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9615-501X","authenticated-orcid":false,"given":"Xinping","family":"Xiao","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,6,3]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2008.112"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2013.08.272"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2012.12.007"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.11.003"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.10.023"},{"key":"e_1_2_9_6_2","first-page":"64","article-title":"A short-term traffic flow forecast algorithm based on double seasonal time series","volume":"45","author":"Qiu D.-G.","year":"2013","journal-title":"Journal of Sichuan University (Engineering Science Edition)"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-18804"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/5721058"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.3141\/2489-11"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2015.09.105"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2513411"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1181-7"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2018.1563829"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2016.09.041"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11071-016-2677-5"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.12.031"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.02.012"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2682855"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2018.05.009"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2889814"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2018.05.013"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.02.006"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2010.12.032"},{"volume-title":"Estimate and Decision of Grey System","year":"2002","author":"Deng J. 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