{"type":"ScholarlyArticle","title":"STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting","authors":[{"name":"Lei Bai","slug":"lei-bai","given":"Lei","family":"Bai","affiliation":"University of New South Wales"},{"name":"Lina Yao","slug":"lina-yao","given":"Lina","family":"Yao","affiliation":"University of New South Wales"},{"name":"Salil S. Kanhere","slug":"salil-s-kanhere","given":"Salil S.","family":"Kanhere","affiliation":"University of New South Wales"},{"name":"Xianzhi Wang","slug":"xianzhi-wang","given":"Xianzhi","family":"Wang","affiliation":"University of Technology Sydney"},{"name":"Quan Z. Sheng","slug":"quan-z-sheng","given":"Quan Z.","family":"Sheng","affiliation":"Macquarie University"}],"abstract":"Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.","doi":"10.24963/ijcai.2019/274","url":"https://ijcai.org/proceedings/2019/274","pdf":"https://ijcai.org/proceedings/2019/0274.pdf","markdown":"https://ijcai.org/proceedings/2019/274.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/274","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":274,"pages":{"first":1981,"last":1987},"keywords":["Machine Learning: Data Mining","Machine Learning: Deep Learning","Multidisciplinary Topics and Applications: Transportation"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2019] Lei Bai, Lina Yao, Salil S Kanhere, Zheng Yang, Jing Chu, and Xianzhi Wang. 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