{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:39:35Z","timestamp":1792514375484,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,4,20]]},"DOI":"10.1145\/3366423.3380186","type":"proceedings-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T08:11:44Z","timestamp":1588579904000},"page":"1082-1092","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":534,"title":["Traffic Flow Prediction via Spatial Temporal Graph Neural Network"],"prefix":"10.1145","author":[{"given":"Xiaoyang","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Ma","sequence":"additional","affiliation":[{"name":"Michigan State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Michigan State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Jin","sequence":"additional","affiliation":[{"name":"Michigan State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiliang","family":"Tang","sequence":"additional","affiliation":[{"name":"Michigan State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiyan","family":"Jia","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Yu","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in Neural Information Processing Systems. 1993\u20132001.  James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in Neural Information Processing Systems. 1993\u20132001."},{"key":"e_1_3_2_1_2_1","unstructured":"Jimmy\u00a0Lei Ba Jamie\u00a0Ryan Kiros and Geoffrey\u00a0E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450(2016).  Jimmy\u00a0Lei Ba Jamie\u00a0Ryan Kiros and Geoffrey\u00a0E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450(2016)."},{"key":"e_1_3_2_1_3_1","volume-title":"Linked: How everything is connected to everything else and what it means. Plume.","author":"Barabasi Albert-Laszlo","year":"2003","unstructured":"Albert-Laszlo Barabasi . 2003 . Linked: How everything is connected to everything else and what it means. Plume. Albert-Laszlo Barabasi. 2003. Linked: How everything is connected to everything else and what it means. Plume."},{"key":"e_1_3_2_1_4_1","unstructured":"Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203(2013).  Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203(2013)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/3015812.3015982"},{"key":"e_1_3_2_1_6_1","volume-title":"Transportation systems engineering: theory and methods. Vol.\u00a049","author":"Cascetta Ennio","unstructured":"Ennio Cascetta . 2013. Transportation systems engineering: theory and methods. Vol.\u00a049 . Springer Science & Business Media . Ennio Cascetta. 2013. Transportation systems engineering: theory and methods. Vol.\u00a049. Springer Science & Business Media."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3141\/1748-12"},{"key":"e_1_3_2_1_8_1","unstructured":"Kyunghyun Cho Bart Van\u00a0Merri\u00ebnboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078(2014).  Kyunghyun Cho Bart Van\u00a0Merri\u00ebnboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078(2014)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.279188"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1083784.1083791"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2503792.2503797"},{"key":"e_1_3_2_1_13_1","unstructured":"William\u00a0L Hamilton Rex Ying and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584(2017).  William\u00a0L Hamilton Rex Ying and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584(2017)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_15_1","unstructured":"Sepp Hochreiter Yoshua Bengio Paolo Frasconi J\u00fcrgen Schmidhuber 2001. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies.  Sepp Hochreiter Yoshua Bengio Paolo Frasconi J\u00fcrgen Schmidhuber 2001. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies."},{"key":"e_1_3_2_1_16_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ), 1735\u20131780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735\u20131780."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2611567"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2396761.2398448"},{"key":"e_1_3_2_1_19_1","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016).  Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2017.04.417"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12544-015-0170-8"},{"key":"e_1_3_2_1_22_1","volume-title":"Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361, 10(1995)","author":"LeCun Yann","year":"1995","unstructured":"Yann LeCun , Yoshua Bengio , 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361, 10(1995) , 1995 . Yann LeCun, Yoshua Bengio, 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361, 10(1995), 1995."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11274"},{"key":"e_1_3_2_1_24_1","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926(2017).  Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926(2017)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2247040"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2012.04.001"},{"key":"e_1_3_2_1_27_1","unstructured":"Peter\u00a0J Liu Mohammad Saleh Etienne Pot Ben Goodrich Ryan Sepassi Lukasz Kaiser and Noam Shazeer. 2018. Generating wikipedia by summarizing long sequences. arXiv preprint arXiv:1801.10198(2018).  Peter\u00a0J Liu Mohammad Saleh Etienne Pot Ben Goodrich Ryan Sepassi Lukasz Kaiser and Noam Shazeer. 2018. Generating wikipedia by summarizing long sequences. arXiv preprint arXiv:1801.10198(2018)."},{"key":"e_1_3_2_1_28_1","volume-title":"New introduction to multiple time series analysis","author":"L\u00fctkepohl Helmut","unstructured":"Helmut L\u00fctkepohl . 2005. New introduction to multiple time series analysis . Springer Science & Business Media . Helmut L\u00fctkepohl. 2005. New introduction to multiple time series analysis. Springer Science & Business Media."},{"key":"e_1_3_2_1_29_1","first-page":"865","article-title":"Traffic flow prediction with big data: a deep learning approach","volume":"16","author":"Lv Yisheng","year":"2014","unstructured":"Yisheng Lv , Yanjie Duan , Wenwen Kang , Zhengxi Li , and Fei-Yue Wang . 2014 . Traffic flow prediction with big data: a deep learning approach . IEEE Transactions on Intelligent Transportation Systems 16 , 2(2014), 865 \u2013 873 . Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. 2014. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems 16, 2(2014), 865\u2013873.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2018.07.004"},{"key":"e_1_3_2_1_31_1","volume-title":"The structure and dynamics of networks","author":"Newman Mark\u00a0Ed","unstructured":"Mark\u00a0Ed Newman , Albert- L\u00e1szl\u00f3\u00a0Ed Barab\u00e1si , and Duncan\u00a0 J Watts . 2006. The structure and dynamics of networks . Princeton university press . Mark\u00a0Ed Newman, Albert-L\u00e1szl\u00f3\u00a0Ed Barab\u00e1si, and Duncan\u00a0J Watts. 2006. The structure and dynamics of networks.Princeton university press."},{"key":"e_1_3_2_1_32_1","unstructured":"Niki Parmar Ashish Vaswani Jakob Uszkoreit \u0141ukasz Kaiser Noam Shazeer Alexander Ku and Dustin Tran. 2018. Image transformer. arXiv preprint arXiv:1802.05751(2018).  Niki Parmar Ashish Vaswani Jakob Uszkoreit \u0141ukasz Kaiser Noam Shazeer Alexander Ku and Dustin Tran. 2018. Image transformer. arXiv preprint arXiv:1802.05751(2018)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_1_35_1","volume-title":"A tutorial on support vector regression. Statistics and computing 14, 3","author":"Smola J","year":"2004","unstructured":"Alex\u00a0 J Smola and Bernhard Sch\u00f6lkopf . 2004. A tutorial on support vector regression. Statistics and computing 14, 3 ( 2004 ), 199\u2013222. Alex\u00a0J Smola and Bernhard Sch\u00f6lkopf. 2004. A tutorial on support vector regression. Statistics and computing 14, 3 (2004), 199\u2013222."},{"key":"e_1_3_2_1_36_1","unstructured":"Ilya Sutskever Oriol Vinyals and Quoc\u00a0V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems. 3104\u20133112.  Ilya Sutskever Oriol Vinyals and Quoc\u00a0V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems. 3104\u20133112."},{"key":"e_1_3_2_1_37_1","volume-title":"International Conference on Machine Learning. 2071\u20132080","author":"Trouillon Th\u00e9o","year":"2016","unstructured":"Th\u00e9o Trouillon , Johannes Welbl , Sebastian Riedel , \u00c9ric Gaussier , and Guillaume Bouchard . 2016 . Complex embeddings for simple link prediction . In International Conference on Machine Learning. 2071\u20132080 . Th\u00e9o Trouillon, Johannes Welbl, Sebastian Riedel, \u00c9ric Gaussier, and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In International Conference on Machine Learning. 2071\u20132080."},{"key":"e_1_3_2_1_38_1","unstructured":"Claudia Ulbricht. 1994. Multi-recurrent networks for traffic forecasting. In AAAI. 883\u2013888.  Claudia Ulbricht. 1994. Multi-recurrent networks for traffic forecasting. In AAAI. 883\u2013888."},{"key":"e_1_3_2_1_39_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998\u20136008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998\u20136008."},{"key":"e_1_3_2_1_40_1","volume-title":"AIP Conference Proceedings, Vol.\u00a01522","author":"Kamarul\u00a0Ariffin Wan\u00a0Ahmad Wan","year":"2013","unstructured":"Wan Kamarul\u00a0Ariffin Wan\u00a0Ahmad and Sabri Ahmad . 2013 . Arima model and exponential smoothing method: A comparison . In AIP Conference Proceedings, Vol.\u00a01522 . AIP, 1312\u20131321. Wan Kamarul\u00a0Ariffin Wan\u00a0Ahmad and Sabri Ahmad. 2013. Arima model and exponential smoothing method: A comparison. In AIP Conference Proceedings, Vol.\u00a01522. AIP, 1312\u20131321."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2754499"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2004.03.003"},{"key":"e_1_3_2_1_43_1","unstructured":"Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang and Philip\u00a0S Yu. 2019. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596(2019).  Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang and Philip\u00a0S Yu. 2019. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596(2019)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.87"},{"key":"e_1_3_2_1_46_1","volume-title":"Network representation learning: A survey","author":"Zhang Daokun","year":"2018","unstructured":"Daokun Zhang , Jie Yin , Xingquan Zhu , and Chengqi Zhang . 2018. Network representation learning: A survey . IEEE transactions on Big Data( 2018 ). Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. 2018. Network representation learning: A survey. IEEE transactions on Big Data(2018)."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00702-0"},{"key":"e_1_3_2_1_48_1","volume-title":"Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294(2018).","author":"Zhang Jiani","year":"2018","unstructured":"Jiani Zhang , Xingjian Shi , Junyuan Xie , Hao Ma , Irwin King , and Dit-Yan Yeung . 2018 . Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294(2018). Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung. 2018. Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294(2018)."},{"key":"e_1_3_2_1_49_1","volume-title":"T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction","author":"Zhao Ling","year":"2019","unstructured":"Ling Zhao , Yujiao Song , Chao Zhang , Yu Liu , Pu Wang , Tao Lin , Min Deng , and Haifeng Li. 2019. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction . IEEE Transactions on Intelligent Transportation Systems ( 2019 ). Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. 2019. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems (2019)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00911"}],"event":{"name":"WWW '20: The Web Conference 2020","location":"Taipei Taiwan","acronym":"WWW '20","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of The Web Conference 2020"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3366423.3380186","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3366423.3380186","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:33:00Z","timestamp":1750199580000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3366423.3380186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,20]]},"references-count":50,"alternative-id":["10.1145\/3366423.3380186","10.1145\/3366423"],"URL":"https:\/\/doi.org\/10.1145\/3366423.3380186","relation":{},"subject":[],"published":{"date-parts":[[2020,4,20]]},"assertion":[{"value":"2020-04-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}