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Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.<\/jats:p>","DOI":"10.3390\/s17071501","type":"journal-article","created":{"date-parts":[[2017,6,27]],"date-time":"2017-06-27T02:58:05Z","timestamp":1498532285000},"page":"1501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":545,"title":["Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks"],"prefix":"10.3390","volume":"17","author":[{"given":"Haiyang","family":"Yu","sequence":"first","affiliation":[{"name":"School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China"}]},{"given":"Zhihai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China"}]},{"given":"Shuqin","family":"Wang","sequence":"additional","affiliation":[{"name":"Passenger Vehicle EE Development Department, China FAW R&amp;D Center, Changchun 130011, China"}]},{"given":"Yunpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China"}]},{"given":"Xiaolei","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1109\/TITS.2013.2290285","article-title":"Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction","volume":"15","author":"Asif","year":"2014","journal-title":"IEEE Trans. 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