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Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>Traffic flow prediction is crucial for public safety and traffic management, and remains a big challenge because of many complicated factors, e.g., multiple spatio-temporal dependencies, holidays, and weather. Some work leveraged 2D convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to explore spatial relations and temporal relations, respectively, which outperformed the classical approaches. However, it is hard for these work to model spatio-temporal relations jointly. To tackle this, some studies utilized LSTMs to connect high-level layers of CNNs, but left the spatio-temporal correlations not fully exploited in low-level layers. In this work, we propose novel spatio-temporal CNNs to extract spatio-temporal features simultaneously from low-level to high-level layers, and propose a novel gated scheme to control the spatio-temporal features that should be propagated through the hierarchy of layers. Based on these, we propose an end-to-end framework, multiple gated spatio-temporal CNNs (MGSTC), for citywide traffic flow prediction. MGSTC can explore multiple spatio-temporal dependencies through multiple gated spatio-temporal CNN branches, and combine the spatio-temporal features with external factors dynamically. Extensive experiments on two real traffic datasets demonstrates that MGSTC outperforms other state-of-the-art baselines.<\/jats:p>","DOI":"10.1145\/3385414","type":"journal-article","created":{"date-parts":[[2020,5,30]],"date-time":"2020-05-30T12:25:16Z","timestamp":1590841516000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":215,"title":["Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1389-0148","authenticated-orcid":false,"given":"Cen","family":"Chen","sequence":"first","affiliation":[{"name":"Hunan University and Infocomm for Research Institute, Singapore"}]},{"given":"Kenli","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, China"}]},{"given":"Sin G.","family":"Teo","sequence":"additional","affiliation":[{"name":"Infocomm for Research Institute, Singapore"}]},{"given":"Xiaofeng","family":"Zou","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, China"}]},{"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"State University of New York and Hunan University, Changsha, China"}]},{"given":"Zeng","family":"Zeng","sequence":"additional","affiliation":[{"name":"Infocomm for Research Institute, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2020,5,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2337238"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2017.8317913"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2290285"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1970.10481180"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2690673"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00107"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301485"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.06.021"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the International Conference on Machine Learning. 933--941","author":"Dauphin Yann N.","year":"2017"},{"key":"e_1_2_1_12_1","volume-title":"Nihan","author":"Davis Gary A.","year":"1991"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-018-1291-x"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.17"},{"key":"e_1_2_1_15_1","unstructured":"Shengdong Du Tianrui Li Xun Gong Zeng Yu and Shi-Jinn Horng. 2018. 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