{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:20:16Z","timestamp":1773771616877,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20230815"],"award-info":[{"award-number":["BK20230815"]}]},{"name":"National Science and Technology Major Project","award":["2021ZD0114200"],"award-info":[{"award-number":["2021ZD0114200"]}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100004608","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61925202"],"award-info":[{"award-number":["61925202"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Provincial Key Research and Development Program","award":["E2022065-1, BE2022065-3"],"award-info":[{"award-number":["E2022065-1, BE2022065-3"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3672030","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"1781-1792","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3511-5559","authenticated-orcid":false,"given":"Li","family":"Lin","sequence":"first","affiliation":[{"name":"Southeast University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0707-1737","authenticated-orcid":false,"given":"Zhiqiang","family":"Lu","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6838-1151","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1180-8078","authenticated-orcid":false,"given":"Yunhuai","family":"Liu","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3682-4290","authenticated-orcid":false,"given":"Zhiqing","family":"Hong","sequence":"additional","affiliation":[{"name":"Rutgers University, Piscataway, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9783-6389","authenticated-orcid":false,"given":"Haotian","family":"Wang","sequence":"additional","affiliation":[{"name":"JD Logistics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2766-1135","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems","author":"Bai Lei","year":"2020","unstructured":"Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, Vol. 33 (2020), 17804--17815."},{"key":"e_1_3_2_2_2_1","volume-title":"NIPS 2014 Workshop on Deep Learning","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.58"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482120"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2993798"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330896"},{"key":"e_1_3_2_2_7_1","volume-title":"Support vector regression machines. Advances in neural information processing systems","author":"Drucker Harris","year":"1996","unstructured":"Harris Drucker, Christopher J Burges, Linda Kaufman, Alex Smola, and Vladimir Vapnik. 1996. Support vector regression machines. Advances in neural information processing systems, Vol. 9 (1996)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-020-00800-3"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539139"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00281"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462898"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-80475-6_16"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498452"},{"key":"e_1_3_2_2_16_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJU4ayYgl","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_2_2_17_1","volume-title":"Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Li Fuxian","year":"2021","unstructured":"Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, and Yong Li. 2021. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data (TKDD) (2021)."},{"key":"e_1_3_2_2_18_1","volume-title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations.","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098180"},{"key":"e_1_3_2_2_20_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Nie Yuqi","year":"2022","unstructured":"Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2022. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16556"},{"key":"e_1_3_2_2_22_1","volume-title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112","author":"Shao Zezhi","year":"2022","unstructured":"Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S Jensen. 2022. Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112 (2022)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441750"},{"key":"e_1_3_2_2_24_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_25_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJXMpikCZ","author":"Cucurull Guillem","year":"2018","unstructured":"Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.83"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20342"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00154"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"e_1_3_2_2_30_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Wu Haixu","year":"2022","unstructured":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2022. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467170"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330887"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00037"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2021.668711"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"e_1_3_2_2_38_1","volume-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 4431--4437","author":"Zhang Mingyang","year":"2021","unstructured":"Mingyang Zhang, Tong Li, Yong Li, and Pan Hui. 2021. Multi-view joint graph representation learning for urban region embedding. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 4431--4437."},{"key":"e_1_3_2_2_39_1","volume-title":"DAC-ML: Domain Adaptable Continuous Meta-Learning for Urban Dynamics Prediction. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 906--915","author":"Zhang Xin","year":"2021","unstructured":"Xin Zhang, Yanhua Li, Xun Zhou, Oren Mangoubi, Ziming Zhang, Vincent Filardi, and Jun Luo. 2021. DAC-ML: Domain Adaptable Continuous Meta-Learning for Urban Dynamics Prediction. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 906--915."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00187"},{"key":"e_1_3_2_2_41_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, Vol. 21, 9 (2019), 3848--3858."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_2_43_1","volume-title":"International Conference on Machine Learning. PMLR, 27268--27286","author":"Zhou Tian","year":"2022","unstructured":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268--27286."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159682"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672030","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3672030","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:06Z","timestamp":1750291566000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672030"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":44,"alternative-id":["10.1145\/3637528.3672030","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3672030","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}