{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:50:45Z","timestamp":1774425045115,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":12,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,11,6]],"date-time":"2018-11-06T00:00:00Z","timestamp":1541462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,11,6]]},"DOI":"10.1145\/3274895.3274896","type":"proceedings-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T13:19:29Z","timestamp":1542201569000},"page":"397-400","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":240,"title":["Bike flow prediction with multi-graph convolutional networks"],"prefix":"10.1145","author":[{"given":"Di","family":"Chai","sequence":"first","affiliation":[{"name":"Clustar, Beijing, China and Hong Kong University of Science and Technology, China"}]},{"given":"Leye","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China and Hong Kong University of Science and Technology, China"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2018,11,6]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203","author":"Bruna Joan","year":"2013","unstructured":"Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann LeCun . Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 , 2013 . Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013."},{"key":"e_1_3_2_1_2_1","volume-title":"Bike flow prediction with multi-graph convolutional networks. arXiv preprint arXiv:1807.10934","author":"Chai Di","year":"2018","unstructured":"Di Chai , Leye Wang , and Qiang Yang . Bike flow prediction with multi-graph convolutional networks. arXiv preprint arXiv:1807.10934 , 2018 . Di Chai, Leye Wang, and Qiang Yang. Bike flow prediction with multi-graph convolutional networks. arXiv preprint arXiv:1807.10934, 2018."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971652"},{"key":"e_1_3_2_1_4_1","first-page":"3844","volume-title":"Advances in Neural Information Processing Systems 29","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard , Xavier Bresson , and Pierre Vandergheynst . Convolutional neural networks on graphs with fast localized spectral filtering. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors , Advances in Neural Information Processing Systems 29 , pages 3844 -- 3852 . Curran Associates, Inc. , 2016 . Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, pages 3844--3852. Curran Associates, Inc., 2016."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_6_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 , 2014 . Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014."},{"key":"e_1_3_2_1_7_1","volume-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. international conference on learning representations","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li , Rose Yu , Cyrus Shahabi , and Yan Liu . Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. international conference on learning representations , 2018 . Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. international conference on learning representations, 2018."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2820783.2820837"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/SmartCity.2015.63"},{"key":"e_1_3_2_1_10_1","volume-title":"Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results. Journal of transportation engineering, 129(6):664--672","author":"Williams Billy M","year":"2003","unstructured":"Billy M Williams and Lester A Hoel . Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results. Journal of transportation engineering, 129(6):664--672 , 2003 . Billy M Williams and Lester A Hoel. Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results. Journal of transportation engineering, 129(6):664--672, 2003."},{"key":"e_1_3_2_1_11_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. international joint conference on artificial intelligence","author":"Yu Bing","year":"2018","unstructured":"Bing Yu , Haoteng Yin , and Zhanxing Zhu . Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. international joint conference on artificial intelligence , 2018 . Bing Yu, Haoteng Yin, and Zhanxing Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. international joint conference on artificial intelligence, 2018."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298239.3298479"}],"event":{"name":"SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","location":"Seattle Washington","acronym":"SIGSPATIAL '18","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"]},"container-title":["Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3274895.3274896","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3274895.3274896","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:57:49Z","timestamp":1750208269000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3274895.3274896"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,6]]},"references-count":12,"alternative-id":["10.1145\/3274895.3274896","10.1145\/3274895"],"URL":"https:\/\/doi.org\/10.1145\/3274895.3274896","relation":{},"subject":[],"published":{"date-parts":[[2018,11,6]]},"assertion":[{"value":"2018-11-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}