{"type":"ScholarlyArticle","title":"Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns","authors":[{"name":"Ali Zonoozi","slug":"ali-zonoozi","given":"Ali","family":"Zonoozi","affiliation":"School of Computer Science and Engineering , Nanyang Technological University"},{"name":"Jung-jae Kim","slug":"jung-jae-kim","given":"Jung-jae","family":"Kim","affiliation":"Institute for Infocomm Research , A-STAR"},{"name":"Xiao-Li Li","slug":"xiao-li-li","given":"Xiao-Li","family":"Li","affiliation":"Institute for Infocomm Research , A-STAR"},{"name":"Gao Cong","slug":"gao-cong","given":"Gao","family":"Cong","affiliation":"School of Computer Science and Engineering , Nanyang Technological University"}],"abstract":"Time-series forecasting in geo-spatial domains has important applications, including urban planning, traffic management and behavioral analysis. We observed recurring periodic patterns in some spatio-temporal data, which were not considered explicitly by previous non-linear works. To address this lack, we propose novel `Periodic-CRN' (PCRN) method, which adapts convolutional recurrent network (CRN) to accurately capture spatial and temporal correlations, learns and incorporates explicit periodic representations, and can be optimized with multi-step ahead prediction. We show that PCRN consistently outperforms the state-of-the-art methods for crowd density prediction across two taxi datasets from Beijing and Singapore.","doi":"10.24963/ijcai.2018/519","url":"https://ijcai.org/proceedings/2018/519","pdf":"https://ijcai.org/proceedings/2018/0519.pdf","markdown":"https://ijcai.org/proceedings/2018/519.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/519","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":519,"pages":{"first":3732,"last":3738},"keywords":["Machine Learning: Neural Networks","Machine Learning Applications: Applications of Supervised Learning","Machine Learning: Time-series;Data Streams","Machine Learning: Machine Learning","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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