{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:00:36Z","timestamp":1772906436246,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T00:00:00Z","timestamp":1531958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"in part by the Office of Naval Research","award":["N000140811186"],"award-info":[{"award-number":["N000140811186"]}]},{"name":"the National Grand Fundamental Research 973 Program of China","award":["2014CB340402"],"award-info":[{"award-number":["2014CB340402"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,7,19]]},"DOI":"10.1145\/3219819.3219931","type":"proceedings-article","created":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T13:05:12Z","timestamp":1532005512000},"page":"724-733","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":49,"title":["StepDeep"],"prefix":"10.1145","author":[{"given":"Bilong","family":"Shen","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Xiaodan","family":"Liang","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}]},{"given":"Yufeng","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}]},{"given":"Miaofeng","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, He Fei, China"}]},{"given":"Weimin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Kathleen M.","family":"Carley","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}]}],"member":"320","published-online":{"date-parts":[[2018,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2543581.2543584"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2298892"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_4_1","unstructured":"Adam Coates and Andrew Y Ng. 2011. Selecting receptive fields in deep networks. In Advances in Neural Information Processing Systems. 2528--2536.   Adam Coates and Andrew Y Ng. 2011. Selecting receptive fields in deep networks. In Advances in Neural Information Processing Systems. 2528--2536."},{"key":"e_1_3_2_1_5_1","volume-title":"Masoud Hamedi, and MAUT Center.","author":"Ghoseiri Keivan","year":"2011","unstructured":"Keivan Ghoseiri , Ali Ebadollahzadeh Haghani , Masoud Hamedi, and MAUT Center. 2011 . Real-time rideshare matching problem. Mid-Atlantic Universities Transportation Center Berkeley . Keivan Ghoseiri, Ali Ebadollahzadeh Haghani, Masoud Hamedi, and MAUT Center. 2011. Real-time rideshare matching problem. Mid-Atlantic Universities Transportation Center Berkeley."},{"key":"e_1_3_2_1_6_1","volume-title":"Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation","author":"Huang Binxuan","unstructured":"Binxuan Huang and Kathleen M Carley . 2017. On Predicting Geolocation of Tweets using Convolutional Neural Networks International Conference on Social Computing , Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation . Springer , 281--291. Binxuan Huang and Kathleen M Carley. 2017. On Predicting Geolocation of Tweets using Convolutional Neural Networks International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. Springer, 281--291."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2670560"},{"key":"e_1_3_2_1_8_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik","year":"2014","unstructured":"Diederik Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623681"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544843"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2876480.2876483"},{"key":"e_1_3_2_1_14_1","article-title":"a. Survey on Dynamic Ride Sharing in Big Data Era","volume":"54","author":"Shen Bilong","year":"2017","unstructured":"Bilong Shen , Ying Zhao , Yan Huang , and Weimin Zheng . 2017 a. Survey on Dynamic Ride Sharing in Big Data Era . Journal of Computer Research and Development , Vol. 54 , 1, Article 34 (2017), 15 pages. Bilong Shen, Ying Zhao, Yan Huang, and Weimin Zheng. 2017 a. Survey on Dynamic Ride Sharing in Big Data Era. Journal of Computer Research and Development, Vol. 54, 1, Article 34 (2017), 15 pages.","journal-title":"Journal of Computer Research and Development"},{"key":"e_1_3_2_1_15_1","volume-title":"ICDE 2017","author":"Shen Bilong","year":"2017","unstructured":"Bilong Shen , Ying Zhao , Guoliang Li , Weimin Zheng , Yue Qin , Bo Yuan , and Yongming Rao . 2017 b. V-Tree: Efficient kNN Search on Moving Objects with Road-Network Constraints 33rd IEEE International Conference on Data Engineering , ICDE 2017 , San Diego, CA, USA, April 19--22 , 2017. 609--620. Bilong Shen, Ying Zhao, Guoliang Li, Weimin Zheng, Yue Qin, Bo Yuan, and Yongming Rao. 2017 b. V-Tree: Efficient kNN Search on Moving Objects with Road-Network Constraints 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, April 19--22, 2017. 609--620."},{"key":"e_1_3_2_1_16_1","unstructured":"New York City Taxi and Limousine Commission (TLC). 2017. The yellow taxi trip records. (2017). http:\/\/www.nyc.gov\/html\/tlc\/html\/about\/trip_record_data.shtml  New York City Taxi and Limousine Commission (TLC). 2017. The yellow taxi trip records. (2017). http:\/\/www.nyc.gov\/html\/tlc\/html\/about\/trip_record_data.shtml"},{"key":"e_1_3_2_1_17_1","volume-title":"Benefits of depth in neural networks. arXiv preprint arXiv:1602.04485","author":"Telgarsky Matus","year":"2016","unstructured":"Matus Telgarsky . 2016. Benefits of depth in neural networks. arXiv preprint arXiv:1602.04485 ( 2016 ). Matus Telgarsky. 2016. Benefits of depth in neural networks. arXiv preprint arXiv:1602.04485 (2016)."},{"key":"e_1_3_2_1_18_1","volume-title":"Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning","author":"Tieleman Tijmen","year":"2012","unstructured":"Tijmen Tieleman and Geoffrey Hinton . 2012. Lecture 6.5-rmsprop : Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning , Vol. 4 , 2 ( 2012 ), 26--31. Tijmen Tieleman and Geoffrey Hinton. 2012. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, Vol. 4, 2 (2012), 26--31."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_2_1_20_1","volume-title":"DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks 33rd IEEE International Conference on Data Engineering, ICDE 2017","author":"Wang Dong","year":"2017","unstructured":"Dong Wang , Wei Cao , Jian Li , and Jieping Ye . 2017 . DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks 33rd IEEE International Conference on Data Engineering, ICDE 2017 , San Diego, CA, USA, April 19--22 , 2017. 243--254. Dong Wang, Wei Cao, Jian Li, and Jieping Ye. 2017. DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, April 19--22, 2017. 243--254."},{"key":"e_1_3_2_1_21_1","volume-title":"2016 IEEE 16th International Conference on. IEEE, 499--508","author":"Wang Jingyuan","year":"2016","unstructured":"Jingyuan Wang , Qian Gu , Junjie Wu , Guannan Liu , and Zhang Xiong . 2016 . Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method Data Mining (ICDM) , 2016 IEEE 16th International Conference on. IEEE, 499--508 . Jingyuan Wang, Qian Gu, Junjie Wu, Guannan Liu, and Zhang Xiong. 2016. Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 499--508."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939799"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906408"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052577"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1869790.1869807"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.153"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2328231"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629592"}],"event":{"name":"KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"London United Kingdom","acronym":"KDD '18","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 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3219819.3219931","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3219819.3219931","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:07:21Z","timestamp":1750212441000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3219819.3219931"}},"subtitle":["A Novel Spatial-temporal Mobility Event Prediction Framework based on Deep Neural Network"],"short-title":[],"issued":{"date-parts":[[2018,7,19]]},"references-count":28,"alternative-id":["10.1145\/3219819.3219931","10.1145\/3219819"],"URL":"https:\/\/doi.org\/10.1145\/3219819.3219931","relation":{},"subject":[],"published":{"date-parts":[[2018,7,19]]},"assertion":[{"value":"2018-07-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}