{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T13:39:58Z","timestamp":1771076398103,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation","award":["U1609217"],"award-info":[{"award-number":["U1609217"]}]},{"name":"National Natural Science Foundation","award":["61773324"],"award-info":[{"award-number":["61773324"]}]},{"name":"National Natural Science Foundation","award":["61672399"],"award-info":[{"award-number":["61672399"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330646","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"3132-3142","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":106,"title":["UrbanFM"],"prefix":"10.1145","author":[{"given":"Yuxuan","family":"Liang","sequence":"first","affiliation":[{"name":"Xidian University &amp; National University of Singapore, Xi'an, China"}]},{"given":"Kun","family":"Ouyang","sequence":"additional","affiliation":[{"name":"National University of Singapore &amp; SAP Machine Learning Applications, Singapore, Singapore"}]},{"given":"Lin","family":"Jing","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"given":"Sijie","family":"Ruan","sequence":"additional","affiliation":[{"name":"Xidian University &amp; JD Intelligent Cities Research, Xi'an, China"}]},{"given":"Ye","family":"Liu","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Junbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"JD Intelligent Cities Research &amp; Southwest Jiaotong University, Beijing, China"}]},{"given":"David S.","family":"Rosenblum","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Yu","family":"Zheng","sequence":"additional","affiliation":[{"name":"JD Intelligent Cities Research, Xidian University, &amp; Southwest Jiaotong University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Hong Chang Dit-Yan Yeung and Yimin Xiong. 2004. Super-resolution through neighbor embedding. In CVPR .  Hong Chang Dit-Yan Yeung and Yimin Xiong. 2004. Super-resolution through neighbor embedding. In CVPR ."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2804277"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.811513"},{"key":"e_1_3_2_1_6_1","volume-title":"Nature","volume":"405","author":"Hahnloser Richard HR","year":"2000","unstructured":"Richard HR Hahnloser , Rahul Sarpeshkar , Misha A Mahowald , Rodney J Douglas , and H Sebastian Seung . 2000 . Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit . Nature , Vol. 405 , 6789 (2000), 947. Richard HR Hahnloser, Rahul Sarpeshkar, Misha A Mahowald, Rodney J Douglas, and H Sebastian Seung. 2000. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature , Vol. 405, 6789 (2000), 947."},{"key":"e_1_3_2_1_7_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016a. Deep residual learning for image recognition. In CVPR. 770--778.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016a. Deep residual learning for image recognition. In CVPR. 770--778."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016b. Identity mappings in deep residual networks. In ECCV. 630--645.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016b. Identity mappings in deep residual networks. In ECCV. 630--645.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2996934"},{"key":"e_1_3_2_1_10_1","volume-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 ( 2015 ). Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)."},{"key":"e_1_3_2_1_11_1","volume-title":"Jung Kwon Lee, and Kyoung Mu Lee","author":"Kim Jiwon","year":"2016","unstructured":"Jiwon Kim , Jung Kwon Lee, and Kyoung Mu Lee . 2016 . Accurate image super-resolution using very deep convolutional networks. In CVPR . 1646--1654. Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016. Accurate image super-resolution using very deep convolutional networks. In CVPR . 1646--1654."},{"key":"e_1_3_2_1_12_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 . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_13_1","volume-title":"Nature","volume":"521","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015 . Deep learning . Nature , Vol. 521 , 7553 (2015), 436. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature , Vol. 521, 7553 (2015), 436."},{"key":"e_1_3_2_1_14_1","first-page":"4","article-title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","volume":"2","author":"Ledig Christian","year":"2017","unstructured":"Christian Ledig , Lucas Theis , Ferenc Husz\u00e1r , Jose Caballero , Andrew Cunningham , Alejandro Acosta , Andrew P Aitken , Alykhan Tejani , Johannes Totz , Zehan Wang , 2017 . Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network .. In CVPR , Vol. 2. 4 . Christian Ledig, Lucas Theis, Ferenc Husz\u00e1r, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et almbox. 2017. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.. In CVPR , Vol. 2. 4.","journal-title":"CVPR"},{"key":"e_1_3_2_1_15_1","volume-title":"HeeAh Cho, Jongbok Kim, and Donju Lee.","author":"Lee Sang Keon","year":"2016","unstructured":"Sang Keon Lee , Heeseo Rain Kwon , HeeAh Cho, Jongbok Kim, and Donju Lee. 2016 . International Case Studies of Smart Cities: Anyang, Republic of Korea . (2016). Sang Keon Lee, Heeseo Rain Kwon, HeeAh Cho, Jongbok Kim, and Donju Lee. 2016. International Case Studies of Smart Cities: Anyang, Republic of Korea. (2016)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304244"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.151"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2003.1203207"},{"key":"e_1_3_2_1_19_1","unstructured":"Wenzhe Shi Jose Caballero Ferenc Husz\u00e1r Johannes Totz Andrew P Aitken Rob Bishop Daniel Rueckert and Zehan Wang. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In ICCV. 1874--1883.  Wenzhe Shi Jose Caballero Ferenc Husz\u00e1r Johannes Totz Andrew P Aitken Rob Bishop Daniel Rueckert and Zehan Wang. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In ICCV. 1874--1883."},{"key":"e_1_3_2_1_20_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623628"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Jian Sun Zongben Xu and Heung-Yeung Shum. 2008. Image super-resolution using gradient profile prior. In CVPR. 1--8.  Jian Sun Zongben Xu and Heung-Yeung Shum. 2008. Image super-resolution using gradient profile prior. In CVPR. 1--8.","DOI":"10.1109\/CVPR.2008.4587659"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539933"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431160500207088"},{"key":"e_1_3_2_1_25_1","volume-title":"Vincent De Smet, and Luc Van Gool","author":"Timofte Radu","year":"2014","unstructured":"Radu Timofte , Vincent De Smet, and Luc Van Gool . 2014 . A Radu Timofte, Vincent De Smet, and Luc Van Gool. 2014. A"},{"key":"e_1_3_2_1_26_1","volume-title":"Asian Conference on Computer Vision. 111--126","unstructured":": Adjusted anchored neighborhood regression for fast super-resolution . In Asian Conference on Computer Vision. 111--126 . : Adjusted anchored neighborhood regression for fast super-resolution. In Asian Conference on Computer Vision. 111--126."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098004"},{"key":"e_1_3_2_1_28_1","volume-title":"Crowd Flow Prediction by Deep Spatio-Temporal Transfer Learning. arXiv preprint arXiv:1802.00386","author":"Wang Leye","year":"2018","unstructured":"Leye Wang , Xu Geng , Xiaojuan Ma , Feng Liu , and Qiang Yang . 2018. Crowd Flow Prediction by Deep Spatio-Temporal Transfer Learning. arXiv preprint arXiv:1802.00386 ( 2018 ). Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, and Qiang Yang. 2018. Crowd Flow Prediction by Deep Spatio-Temporal Transfer Learning. arXiv preprint arXiv:1802.00386 (2018)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2050625"},{"key":"e_1_3_2_1_30_1","volume-title":"Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In The AAAI Conference on Artificial Intelligence. 1655--1661","author":"Zhang Junbo","year":"2017","unstructured":"Junbo Zhang , Yu Zheng , and Dekang Qi . 2017 . Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In The AAAI Conference on Artificial Intelligence. 1655--1661 . Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In The AAAI Conference on Artificial Intelligence. 1655--1661."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2997016"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629592"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","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 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330646","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330646","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:09Z","timestamp":1750208529000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330646"}},"subtitle":["Inferring Fine-Grained Urban Flows"],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":32,"alternative-id":["10.1145\/3292500.3330646","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330646","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}