{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T12:01:51Z","timestamp":1786190511577,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,8,13]],"date-time":"2017-08-13T00:00:00Z","timestamp":1502582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hong Kong CERG projects","award":["621013,16211214,16209715"],"award-info":[{"award-number":["621013,16211214,16209715"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502021,71531001,61421003,61328202"],"award-info":[{"award-number":["61502021,71531001,61421003,61328202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Microsoft Research Asia Gift Grant"},{"name":"Hong Kong RGC Project","award":["N HKUST637\/13"],"award-info":[{"award-number":["N HKUST637\/13"]}]},{"name":"HKUST-SSTSP","award":["FP305"],"award-info":[{"award-number":["FP305"]}]},{"name":"National Grand Fundamental Research 973 Program of China","award":["2014CB340304"],"award-info":[{"award-number":["2014CB340304"]}]},{"name":"NSFC Guang Dong Grant","award":["U1301253"],"award-info":[{"award-number":["U1301253"]}]},{"name":"SKLSDE (BUAA) Open Program","award":["SKLSDE-2016ZX-13"],"award-info":[{"award-number":["SKLSDE-2016ZX-13"]}]},{"name":"Google Faculty Award 2013"},{"name":"ITS","award":["170"],"award-info":[{"award-number":["170"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,8,13]]},"DOI":"10.1145\/3097983.3098018","type":"proceedings-article","created":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T18:35:54Z","timestamp":1501871754000},"page":"1653-1662","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":231,"title":["The Simpler The Better"],"prefix":"10.1145","author":[{"given":"Yongxin","family":"Tong","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"4Paradigm Inc., Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zimu","family":"Zhou","sequence":"additional","affiliation":[{"name":"ETH Zurich, Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Didi Research Institute, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"4Paradigm Inc. & Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieping","family":"Ye","sequence":"additional","affiliation":[{"name":"Didi Research Institute, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weifeng","family":"Lv","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,8,13]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Didi Chuxing. https:\/\/en.wikipedia.org\/wiki\/Didi_Chuxing. Didi Chuxing. https:\/\/en.wikipedia.org\/wiki\/Didi_Chuxing."},{"key":"e_1_3_2_2_2_1","unstructured":"Grab. https:\/\/en.wikipedia.org\/wiki\/Grab_(application). Grab. https:\/\/en.wikipedia.org\/wiki\/Grab_(application)."},{"key":"e_1_3_2_2_3_1","unstructured":"Uber. https:\/\/en.wikipedia.org\/wiki\/Uber_(company). Uber. https:\/\/en.wikipedia.org\/wiki\/Uber_(company)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2013.6728312"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"T. Cover and J. Thomas. 2006. Elements of information theory (2. ed.). T. Cover and J. Thomas. 2006. Elements of information theory (2. ed.).","DOI":"10.1002\/047174882X"},{"key":"e_1_3_2_2_6_1","unstructured":"J. Dean G. Corrado R. Monga K. Chen M. Devin M. Mao A. Senior P. Tucker K. Yang and Q. Le. 2012. Large scale distributed deep networks. In Advances in neural information processing systems. 1232--1240. J. Dean G. Corrado R. Monga K. Chen M. Devin M. Mao A. Senior P. Tucker K. Yang and Q. Le. 2012. Large scale distributed deep networks. In Advances in neural information processing systems. 1232--1240."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"J. Friedman. 2001. Greedy function approximation: a gradien boosting machine. Annals of statistics(2001). J. Friedman. 2001. Greedy function approximation: a gradien boosting machine. Annals of statistics(2001).","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2648584.2648589"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"e_1_3_2_2_10_1","unstructured":"Q. Ho J. Cipar H. Cui S. Lee J. Kim P. Gibbons G. Gibson G. Ganger and E. Xing. 2013. More effective distributed ml via a stale synchronous parallel parameter server. In Advances in neural information processing systems. 1223--1231. Q. Ho J. Cipar H. Cui S. Lee J. Kim P. Gibbons G. Gibson G. Ganger and E. Xing. 2013. More effective distributed ml via a stale synchronous parallel parameter server. In Advances in neural information processing systems. 1223--1231."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2640087.2644155"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2820783.2820837"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2488200"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2262376"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-29934-6_57"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2004.1319997"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Y. Tong Y. Chen Z. Zhou L. Chen J. Wang Q. Yang and J. Ye. 2017. The simpler the better: a unified approach to predicting original taxi demands on large-scale online platforms (Techinical report). http:\/\/www.cse.ust.hk\/~yxtong\/tr_prediction.pdf. (2017). Y. Tong Y. Chen Z. Zhou L. Chen J. Wang Q. Yang and J. Ye. 2017. The simpler the better: a unified approach to predicting original taxi demands on large-scale online platforms (Techinical report). http:\/\/www.cse.ust.hk\/~yxtong\/tr_prediction.pdf. (2017).","DOI":"10.1145\/3097983.3098018"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/2994509.2994523"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498228"},{"key":"e_1_3_2_2_20_1","unstructured":"V. Vapnik and V. Vapnik. 1998. Statistical learning theory. Wiley New York. V. Vapnik and V. Vapnik. 1998. Statistical learning theory. Wiley New York."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Li X. Pan G. Wu Z. Qi G. S. Li D Zhang W. Zhang and Z. Wang. 2012. Prediction of urban human mobility using large-scale taxi traces and its applications. Frontiers of Computer Science in China (2012) 111--121. Li X. Pan G. Wu Z. Qi G. S. Li D Zhang W. Zhang and Z. Wang. 2012. Prediction of urban human mobility using large-scale taxi traces and its applications. Frontiers of Computer Science in China (2012) 111--121.","DOI":"10.1007\/s11704-011-1192-6"},{"key":"e_1_3_2_2_22_1","volume-title":"Dual averaging methods for regularized stochastic learning and online optimization. Journal of Machine Learning Research","author":"Xiao L.","year":"2010","unstructured":"L. Xiao . 2010. Dual averaging methods for regularized stochastic learning and online optimization. Journal of Machine Learning Research ( 2010 ), 2543--2596. L. Xiao. 2010. Dual averaging methods for regularized stochastic learning and online optimization. Journal of Machine Learning Research (2010), 2543--2596."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020462"},{"key":"e_1_3_2_2_24_1","volume-title":"18th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems","author":"Yuan J.","unstructured":"J. Yuan , Y. Zheng , C. Zhang , W. Xie , G. Xie , X.and Sun, and Y. Huang . 2010. T-drive: driving directions based on taxi trajectories . In 18th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems , San Jose, CA, USA, Proceedings. 99--108. J. Yuan, Y. Zheng, C. Zhang, W. Xie, G. Xie, X.and Sun, and Y. Huang. 2010. T-drive: driving directions based on taxi trajectories. In 18th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, San Jose, CA, USA, Proceedings. 99--108."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2030112.2030128"},{"key":"e_1_3_2_2_26_1","volume-title":"ADADELTA: an adaptive learning rate method. arXiv preprint","author":"Zeiler M.","year":"2012","unstructured":"M. Zeiler . 2012. ADADELTA: an adaptive learning rate method. arXiv preprint ( 2012 ). M. Zeiler. 2012. ADADELTA: an adaptive learning rate method. arXiv preprint (2012)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/SCC.2016.51"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840676"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Y. Zheng L. Capra O. Wolfson and H. Yang. 2014. Urban computing: concepts methodologies and applications. ACM Transactions on Intelligent Systems and Technology (2014) 38:1--38:55. Y. Zheng L. Capra O. Wolfson and H. Yang. 2014. Urban computing: concepts methodologies and applications. ACM Transactions on Intelligent Systems and Technology (2014) 38:1--38:55.","DOI":"10.1145\/2629592"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2030112.2030126"}],"event":{"name":"KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"Halifax NS Canada","acronym":"KDD '17","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 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3097983.3098018","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3097983.3098018","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T19:03:43Z","timestamp":1750273423000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3097983.3098018"}},"subtitle":["A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms"],"short-title":[],"issued":{"date-parts":[[2017,8,13]]},"references-count":30,"alternative-id":["10.1145\/3097983.3098018","10.1145\/3097983"],"URL":"https:\/\/doi.org\/10.1145\/3097983.3098018","relation":{},"subject":[],"published":{"date-parts":[[2017,8,13]]},"assertion":[{"value":"2017-08-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}