{"type":"ScholarlyArticle","title":"A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling","authors":[{"name":"Jie Feng","slug":"jie-feng","given":"Jie","family":"Feng","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,"},{"name":"Ziqian Lin","slug":"ziqian-lin","given":"Ziqian","family":"Lin","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,"},{"name":"Tong Xia","slug":"tong-xia","given":"Tong","family":"Xia","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,"},{"name":"Funing Sun","slug":"funing-sun","given":"Funing","family":"Sun","affiliation":"Tencent Inc. , Beijing 100084 , China.; Department of Electronic Engineering , Tsinghua University , Beijing 100084 , China."},{"name":"Diansheng Guo","slug":"diansheng-guo","given":"Diansheng","family":"Guo","affiliation":"Tencent Inc. , Beijing 100084 , China."},{"name":"Yong Li","slug":"yong-li","given":"Yong","family":"Li","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,"}],"abstract":"Population flow prediction is one of the most fundamental components in many applications from urban management to transportation schedule. It is challenging due to the complicated spatial-temporal correlation.While many studies have been done in recent years, they fail to simultaneously and effectively model the spatial correlation and temporal variations among population flows. In this paper, we propose Convolution based Sequential and Cross Network (CSCNet) to solve them. On the one hand, we design a CNN based sequential structure with progressively merging the flow features from different time in different CNN layers to model the spatial-temporal information simultaneously. On the other hand, we make use of the transition flow as the proxy to efficiently and explicitly capture the dynamic correlation between different types of population flows. Extensive experiments on 4 datasets demonstrate that CSCNet outperforms the state-of-the-art baselines by reducing the prediction error around 7.7%∼10.4%.","doi":"10.24963/ijcai.2020/185","url":"https://ijcai.org/proceedings/2020/185","pdf":"https://ijcai.org/proceedings/2020/0185.pdf","markdown":"https://ijcai.org/proceedings/2020/185.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/185","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":185,"pages":{"first":1331,"last":1337},"keywords":["Multidisciplinary Topics and Applications: Transportation","Machine Learning Applications: Environmental","Data Mining: Applications","Data Mining: Mining Spatial, Temporal Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25585/file_25585.m3u8","length_sec":259,"provider":"hls"}],"references":[{"position":1,"raw":"[Box et al., 2015] George EP Box, Gwilym M Jenkins, Gre- gory C Reinsel, and Greta M Ljung. 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