{"type":"ScholarlyArticle","title":"LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks","authors":[{"name":"Rongzhou Huang","slug":"rongzhou-huang","given":"Rongzhou","family":"Huang","affiliation":"Sun Yat-Sen University , Guangzhou , China"},{"name":"Chuyin Huang","slug":"chuyin-huang","given":"Chuyin","family":"Huang","affiliation":"Sun Yat-Sen University , Guangzhou , China"},{"name":"Yubao Liu","slug":"yubao-liu","given":"Yubao","family":"Liu","affiliation":"Sun Yat-Sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Genan Dai","slug":"genan-dai","given":"Genan","family":"Dai","affiliation":"Sun Yat-Sen University , Guangzhou , China"},{"name":"Weiyang Kong","slug":"weiyang-kong","given":"Weiyang","family":"Kong","affiliation":"Sun Yat-Sen University , Guangzhou , China"}],"abstract":"Traffic prediction is a classical spatial-temporal prediction problem with many real-world applications such as intelligent route planning, dynamic traffic management, and smart location-based applications. Due to the high nonlinearity and complexity of traffic data, deep learning approaches have attracted much interest in recent years. However, few methods are satisfied with both long and short-term prediction tasks. Target at the shortcomings of existing studies, in this paper, we propose a novel deep learning framework called Long Short-term Graph Convolutional Networks (LSGCN) to tackle both traffic prediction tasks. In our framework, we propose a new graph attention network called cosAtt, and integrate both cosAtt and graph convolution networks (GCN) into a spatial gated block. By the spatial gated block and gated linear units convolution (GLU), LSGCN can efficiently capture complex spatial-temporal features and obtain stable prediction results. Experiments with three real-world traffic datasets verify the effectiveness of LSGCN.","doi":"10.24963/ijcai.2020/326","url":"https://ijcai.org/proceedings/2020/326","pdf":"https://ijcai.org/proceedings/2020/0326.pdf","markdown":"https://ijcai.org/proceedings/2020/326.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/326","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":"Machine Learning","track":"Main track","number":326,"pages":{"first":2355,"last":2361},"keywords":["Machine Learning: Deep Learning","Data Mining: Mining Spatial, Temporal Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24750/file_24750.m3u8","length_sec":278,"provider":"hls"}],"references":[{"position":1,"raw":"[Ahmed and Cook, 1979] Mohammed S Ahmed and Allen R Cook. 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