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In recent years, by using the wireless sensor network to sense the passenger data in advance, the technique of machine learning and neural networks has been utilized to assist the short\u2010term passenger flow prediction. In this study, building on convolutional neural network (CNN) and long short\u2010term memory network (LSTM), a complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and attention\u2010based CNN\u2010LSTM network to extract both temporal and spatial characteristics of passenger flow data, is proposed. Moreover, the problem of the inaccuracy of the noise part is properly solved by adding the CEEMDAN algorithm to the input layer. With the proposed network structure, the CNN\u2010LSTM network is replaced with the Conv\u2010LSTM network to reduce the information loss and get a further performance improvement. The result shows that 39% performance improvement can be achieved than the case with a single LSTM network, and 28% performance improvement can be achieved than the CNN\u2010LSTM network.<\/jats:p>","DOI":"10.1049\/cmu2.12350","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T03:44:15Z","timestamp":1644378255000},"page":"1253-1263","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Short\u2010term passenger flow forecasting using CEEMDAN meshed CNN\u2010LSTM\u2010attention model under wireless sensor network"],"prefix":"10.1049","volume":"16","author":[{"given":"Jiaxuan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering Tongji University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2974-0972","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering Tongji University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering Tongji University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.5755\/j01.eee.19.3.1232"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"Chauhan N.K. 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