{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:22:03Z","timestamp":1771003323798,"version":"3.50.1"},"reference-count":15,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2021,3,25]]},"abstract":"<jats:p>The traffic index prediction plays an important role in many intelligent transportation applications. The challenge mainly comes from the strong nonlinearities in the changing of traffic index, which is caused by transitions of various traffic states including smooth traffic, congestion, breakdown and recovery. Deep learning performs well in capturing and describing the nonlinear relationship among variables. This paper uses a Sequence to Sequence (Seq2Seq) deep learning framework to predict the traffic index. In the proposed method, we use Long Short Term Memory (LSTM) as the basic circulating unit. And the LSTM units are stacked to extract the time changing characteristic and the periodic characteristic of the input traffic index sequence. Besides, the extracted feature vectors are decoded by another single layer LSTM to predict the traffic index of each time after inputting sequence. Experiments are conducted on Beijing traffic index datasets. The proposed method outperformed Autoregressive Integrated Moving Average Model (ARIMA) and LSTM under some commonly used evaluation metrics.<\/jats:p>","DOI":"10.3233\/jcm-204466","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T11:15:23Z","timestamp":1594725323000},"page":"175-184","source":"Crossref","is-referenced-by-count":1,"title":["Traffic index prediction based on sequence to sequence learning"],"prefix":"10.1177","volume":"21","author":[{"given":"Yueying","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Zhijie","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Jianqin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/JCM-204466_ref1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1631\/FITEE.1500381","article-title":"Real-time road traffic state prediction based on ARIMA and Kalman filter","volume":"18","author":"Xu","year":"2017","journal-title":"Frontiers of Information Technology & Electornic Engineering"},{"key":"10.3233\/JCM-204466_ref2","doi-asserted-by":"crossref","unstructured":"W. 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Li, A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial-temporal data features, in: Future Generation Computer Systems, 2018, pp. 78\u201388.","DOI":"10.1016\/j.future.2018.06.021"},{"issue":"2","key":"10.3233\/JCM-204466_ref9","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1061\/(ASCE)0733-947X(2006)132:2(114)","article-title":"Short-term freeway traffic flow prediction: Bayesian combined neural network approach","volume":"132","author":"Zheng","year":"2006","journal-title":"Journal of Transportation Engineering"},{"issue":"3","key":"10.3233\/JCM-204466_ref10","doi-asserted-by":"crossref","first-page":"707","DOI":"10.3233\/JCM-190017","article-title":"The prediction of foundation pit based on genetic back propagation neural network","volume":"19","author":"Wu","year":"2019","journal-title":"Journal of Computational Methods in Sciences and Engineering"},{"issue":"1","key":"10.3233\/JCM-204466_ref11","first-page":"81","article-title":"Study on short-time traffic flow prediction based on deep learning","volume":"18","author":"Wang","year":"2018","journal-title":"Journal of Transportation Systems Engineering and Information Technology"},{"issue":"10","key":"10.3233\/JCM-204466_ref12","first-page":"2951","article-title":"Research on traffic congestion prediction model based on deep learning","volume":"32","author":"Tan","year":"2015","journal-title":"Application Research of Computers"},{"issue":"11","key":"10.3233\/JCM-204466_ref13","first-page":"77","article-title":"Traffic flow prediction based on deep learning","volume":"30","author":"Liu","year":"2018","journal-title":"Journal of System Simulation"},{"key":"10.3233\/JCM-204466_ref15","doi-asserted-by":"crossref","unstructured":"S. Venugopalan, M. Rohrbach and J. Donahue, Sequence to sequence \u2013 video to text, in: International Conference on Computer Vision, 2015, pp. 4534\u20134542.","DOI":"10.1109\/ICCV.2015.515"},{"key":"10.3233\/JCM-204466_ref16","unstructured":"I. Sutskever, O. Vinyals and Q.V. Le, Sequence to Sequence Learning with Neural Networks, in: Neural Information Processing Systems, 2014, pp. 3104\u20133112."},{"issue":"2","key":"10.3233\/JCM-204466_ref17","doi-asserted-by":"crossref","first-page":"353","DOI":"10.3233\/JCM-180877","article-title":"Generating research of image caption based on improved NIC algorithm","volume":"19","author":"Zhu","year":"2019","journal-title":"Journal of Computational Methods in Sciences and Engineering"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-204466","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:31:55Z","timestamp":1771000315000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-204466"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":15,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jcm-204466","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,25]]}}}