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Knowl. Discov. Data"],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>\n            Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims at predicting the\n            <jats:italic>inflow<\/jats:italic>\n            (the traffic of crowds entering a region in a given time interval) and\n            <jats:italic>outflow<\/jats:italic>\n            (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this article, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, DeepSTN+ employs the\n            <jats:italic>ConvPlus<\/jats:italic>\n            structure to model the long-range spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose a temporal attention-based fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on four real-life datasets demonstrate the superiority of our model, i.e., DeepSTN+ reduces the error of the crowd flow prediction by approximately 10%\u201321% compared with the state-of-the-art baselines.\n          <\/jats:p>","DOI":"10.1145\/3477577","type":"journal-article","created":{"date-parts":[[2021,10,23]],"date-time":"2021-10-23T04:28:40Z","timestamp":1634963320000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Context-aware Spatial-Temporal Neural Network for Citywide Crowd Flow Prediction via Modeling Long-range Spatial Dependency"],"prefix":"10.1145","volume":"16","author":[{"given":"Jie","family":"Feng","sequence":"first","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China"}]},{"given":"Ziqian","family":"Lin","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China"}]},{"given":"Can","family":"Rong","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China"}]},{"given":"Funing","family":"Sun","sequence":"additional","affiliation":[{"name":"Tencent Inc., Beijing, China"}]},{"given":"Diansheng","family":"Guo","sequence":"additional","affiliation":[{"name":"Tencent Inc., Beijing, China"}]},{"given":"Depeng","family":"Jin","sequence":"additional","affiliation":[{"name":"BNRist, Department of Electronic Engineering, Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/561899"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.5555\/3491440.3491625"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/2886521.2886559"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2906365"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1515\/9780691218632"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2996934"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2996934"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"issue":"8","key":"e_1_3_2_10_2","first-page":"2011","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Jie H.","year":"2017","unstructured":"H. Jie, S. Li, S. Gang, and S. Albanie. 2017. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 42, 8 (2017), 2011\u20132023.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3193077.3193082"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_3"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403122"},{"key":"e_1_3_2_15_2","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International Conference on Learning Representations . https:\/\/openreview.net\/forum?id=SJiHXGWAZ."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2820783.2820837"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Y. Liang K. Ouyang Y. Wang Y. Liu and D. S. Rosenblum. 2020. Revisiting Convolutional neural networks for citywide crowd flow analytics. In the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases . Springer.","DOI":"10.1007\/978-3-030-67658-2_33"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011020"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330884"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623653"},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. 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