{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:13:21Z","timestamp":1761808401994,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFB1003804"],"award-info":[{"award-number":["2018YFB1003804"]}]},{"name":"Beijing Nova Program of Science and Technology","award":["Z191100001119031"],"award-info":[{"award-number":["Z191100001119031"]}]},{"name":"Beijing Natural Science Foundation","award":["4182042"],"award-info":[{"award-number":["4182042"]}]},{"name":"The Open Program of Zhejiang Lab","award":["2019KE0AB03"],"award-info":[{"award-number":["2019KE0AB03"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,19]]},"DOI":"10.1145\/3340531.3411920","type":"proceedings-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T05:31:05Z","timestamp":1603085465000},"page":"905-914","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Deep Spatio-Temporal Multiple Domain Fusion Network for Urban Anomalies Detection"],"prefix":"10.1145","author":[{"given":"Ruiqiang","family":"Liu","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Cheng","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Ltd., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haina","family":"Tang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taoyu","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Inferring the Root Cause in Road Traffic Anomalies. In 12th IEEE International Conference on Data Mining, ICDM 2012","author":"Chawla Sanjay","year":"2012","unstructured":"Sanjay Chawla , Yu Zheng , and Jiafeng Hu . 2012 . Inferring the Root Cause in Road Traffic Anomalies. In 12th IEEE International Conference on Data Mining, ICDM 2012 , Brussels, Belgium, December 10--13 , 2012. 141--150. https:\/\/doi.org\/10.1109\/ICDM.2012.104 10.1109\/ICDM.2012.104 Sanjay Chawla, Yu Zheng, and Jiafeng Hu. 2012. Inferring the Root Cause in Road Traffic Anomalies. In 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, December 10--13, 2012. 141--150. https:\/\/doi.org\/10.1109\/ICDM.2012.104"},{"key":"e_1_3_2_2_2_1","volume-title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard , Xavier Bresson , and Pierre Vandergheynst . 2016 . Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016 , December 5 --10 , 2016, Barcelona, Spain.3837--3845. http:\/\/papers.nips.cc\/paper\/6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5--10, 2016, Barcelona, Spain.3837--3845. http:\/\/papers.nips.cc\/paper\/6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.2991\/ijcis.d.200120.001"},{"key":"e_1_3_2_2_4_1","volume-title":"Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016","author":"He Kaiming","year":"2016","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2016 . Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 , Las Vegas, NV, USA, June 27--30 , 2016. 770--778.https:\/\/doi.org\/10.1109\/CVPR.2016.90 10.1109\/CVPR.2016.90 Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27--30, 2016. 770--778.https:\/\/doi.org\/10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983886"},{"key":"e_1_3_2_2_6_1","volume-title":"Proceedings ofthe 32nd International Conference on Machine Learning, ICML 2015","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015 . Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . In Proceedings ofthe 32nd International Conference on Machine Learning, ICML 2015 , Lille, France, 6- -11 July 2015. 448--456. http:\/\/proceedings.mlr.press\/v37\/ioffe15.html Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings ofthe 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6--11 July 2015. 448--456. http:\/\/proceedings.mlr.press\/v37\/ioffe15.html"},{"key":"e_1_3_2_2_7_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. In3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings . http:\/\/arxiv.org\/abs\/1412.6980 Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings. http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_2_8_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings . https:\/\/openreview.net\/forum?id=SJU4ayYgl Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_2_2_9_1","volume-title":"Temporal Convolutional Networks for Action Segmentation and Detection. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017","author":"Lea Colin","year":"2017","unstructured":"Colin Lea , Michael D. Flynn , Ren\u00e9 Vidal , Austin Reiter , and Gregory D. Hager . 2017 . Temporal Convolutional Networks for Action Segmentation and Detection. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 , Honolulu, HI, USA, July 21--26 , 2017 . 1003--1012. https:\/\/doi.org\/10.1109\/CVPR.2017.113 10.1109\/CVPR.2017.113 Colin Lea, Michael D. Flynn, Ren\u00e9 Vidal, Austin Reiter, and Gregory D. Hager. 2017. Temporal Convolutional Networks for Action Segmentation and Detection. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21--26, 2017. 1003--1012. https:\/\/doi.org\/10.1109\/CVPR.2017.113"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.06.016"},{"key":"e_1_3_2_2_11_1","volume-title":"ACFM: ADynamic Spatial-Temporal Network for Traffic Prediction. CoRRabs\/1909.02902(2019). arXiv:1909.02902 http:\/\/arxiv.org\/abs\/1909.02902","author":"Liu Lingbo","year":"2019","unstructured":"Lingbo Liu , Jiajie Zhen , Guanbin Li , Geng Zhan , and Liang Lin . 2019 . ACFM: ADynamic Spatial-Temporal Network for Traffic Prediction. CoRRabs\/1909.02902(2019). arXiv:1909.02902 http:\/\/arxiv.org\/abs\/1909.02902 Lingbo Liu, Jiajie Zhen, Guanbin Li, Geng Zhan, and Liang Lin. 2019. ACFM: ADynamic Spatial-Temporal Network for Traffic Prediction. CoRRabs\/1909.02902(2019). arXiv:1909.02902 http:\/\/arxiv.org\/abs\/1909.02902"},{"key":"e_1_3_2_2_12_1","volume-title":"Taxi Origin-Destination Demand Prediction with Contextualized Spatial-Temporal Network. In IEEE International Conference on Multimedia and Expo, ICME 2019","author":"Qiu Zhilin","year":"2019","unstructured":"Zhilin Qiu , Lingbo Liu , Guanbin Li , Qing Wang , Nong Xiao , and Liang Lin . 2019 . Taxi Origin-Destination Demand Prediction with Contextualized Spatial-Temporal Network. In IEEE International Conference on Multimedia and Expo, ICME 2019 , Shanghai, China, July 8--12 , 2019. 760--765. https:\/\/doi.org\/10.1109\/ICME.2019.00136 10.1109\/ICME.2019.00136 Zhilin Qiu, Lingbo Liu, Guanbin Li, Qing Wang, Nong Xiao, and Liang Lin. 2019. Taxi Origin-Destination Demand Prediction with Contextualized Spatial-Temporal Network. In IEEE International Conference on Multimedia and Expo, ICME 2019, Shanghai, China, July 8--12, 2019. 760--765. https:\/\/doi.org\/10.1109\/ICME.2019.00136"},{"key":"e_1_3_2_2_13_1","volume-title":"Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems","author":"Shi Xingjian","year":"2015","unstructured":"Xingjian Shi , Zhourong Chen , Hao Wang , Dit-Yan Yeung , Wai-Kin Wong , and Wang-chun Woo. 2015. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting . In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015 , December 7--12, 2015, Montreal, Quebec, Canada . 802--810. http:\/\/papers.nips.cc\/paper\/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. 2015. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7--12, 2015, Montreal, Quebec, Canada. 802--810. http:\/\/papers.nips.cc\/paper\/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11309"},{"key":"e_1_3_2_2_15_1","volume-title":"Semantic Exploration of Traffic Dynamics. CoRRabs\/1804.04165","author":"Wu Fei","year":"2018","unstructured":"Fei Wu , Hongjian Wang , and Zhenhui Li. 2018. Semantic Exploration of Traffic Dynamics. CoRRabs\/1804.04165 ( 2018 ). arXiv:1804.04165 http:\/\/arxiv.org\/abs\/1804.04165 Fei Wu, Hongjian Wang, and Zhenhui Li. 2018. Semantic Exploration of Traffic Dynamics. CoRRabs\/1804.04165 (2018). arXiv:1804.04165 http:\/\/arxiv.org\/abs\/1804.04165"},{"volume-title":"Machine Learning and Knowledge Discovery in Databases - European Conference, ECMLPKDD 2017, Skopje, Macedonia, September 18--22, 2017, Proceedings, Part II. 622--638","author":"Wu Xian","key":"e_1_3_2_2_16_1","unstructured":"Xian Wu , Yuxiao Dong , Chao Huang , Jian Xu , Dong Wang , and Nitesh V. Chawla . 2017. UAPD: Predicting Urban Anomalies from Spatial-Temporal Data . In Machine Learning and Knowledge Discovery in Databases - European Conference, ECMLPKDD 2017, Skopje, Macedonia, September 18--22, 2017, Proceedings, Part II. 622--638 . https:\/\/doi.org\/10.1007\/978--3--319--71246--8_38 10.1007\/978--3--319--71246--8_38 Xian Wu, Yuxiao Dong, Chao Huang, Jian Xu, Dong Wang, and Nitesh V. Chawla. 2017. UAPD: Predicting Urban Anomalies from Spatial-Temporal Data. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECMLPKDD 2017, Skopje, Macedonia, September 18--22, 2017, Proceedings, Part II. 622--638. https:\/\/doi.org\/10.1007\/978--3--319--71246--8_38"},{"key":"e_1_3_2_2_17_1","volume-title":"The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI","author":"Yao Huaxiu","year":"2019","unstructured":"Huaxiu Yao , Xianfeng Tang , Hua Wei , Guanjie Zheng , and Zhenhui Li. 2019. Revisiting Spatial-Temporal Similarity : A Deep Learning Framework for Traffic Prediction . In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019 , The Thirty-First Innovative Applications of Artificial Intelligence Conference,IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019.5668--5675. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/4511 Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2019.Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference,IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019.5668--5675. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/4511"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330887"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219822"},{"key":"e_1_3_2_2_21_1","volume-title":"Spatio-Temporal Event Detection Using Dynamic Conditional Random Fields. In IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence","author":"Yin Jie","year":"2009","unstructured":"Jie Yin , Derek Hao Hu , and Qiang Yang . 2009 . Spatio-Temporal Event Detection Using Dynamic Conditional Random Fields. In IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence , Pasadena, California, USA, July 11--17 , 2009. 1321--1326. http:\/\/ijcai.org\/Proceedings\/09\/Papers\/222.pdf Jie Yin, Derek Hao Hu, and Qiang Yang. 2009. Spatio-Temporal Event Detection Using Dynamic Conditional Random Fields. In IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11--17, 2009. 1321--1326. http:\/\/ijcai.org\/Proceedings\/09\/Papers\/222.pdf"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191786"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298239.3298479"},{"key":"e_1_3_2_2_25_1","volume-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019","author":"Zhang Mingyang","year":"2019","unstructured":"Mingyang Zhang , Tong Li , Hongzhi Shi , Yong Li , and Pan Hui . 2019 . A De-composition Approach for Urban Anomaly Detection Across Spatiotemporal Data . In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019 , Macao, China, August 10--16 , 2019. 6043--6049.https:\/\/doi.org\/10.24963\/ijcai.2019\/837 10.24963\/ijcai.2019 Mingyang Zhang, Tong Li, Hongzhi Shi, Yong Li, and Pan Hui. 2019. A De-composition Approach for Urban Anomaly Detection Across Spatiotemporal Data. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10--16, 2019. 6043--6049.https:\/\/doi.org\/10.24963\/ijcai.2019\/837"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2820783.2820813"}],"event":{"name":"CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Virtual Event Ireland","acronym":"CIKM '20"},"container-title":["Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411920","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340531.3411920","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:22Z","timestamp":1750197682000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,19]]},"references-count":26,"alternative-id":["10.1145\/3340531.3411920","10.1145\/3340531"],"URL":"https:\/\/doi.org\/10.1145\/3340531.3411920","relation":{},"subject":[],"published":{"date-parts":[[2020,10,19]]},"assertion":[{"value":"2020-10-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}