{"type":"ScholarlyArticle","title":"Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction","authors":[{"name":"Ruixing Zhang","slug":"ruixing-zhang","given":"Ruixing","family":"Zhang","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"},{"name":"Liangzhe Han","slug":"liangzhe-han","given":"Liangzhe","family":"Han","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"},{"name":"Boyi Liu","slug":"boyi-liu","given":"Boyi","family":"Liu","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"},{"name":"Jiayuan Zeng","slug":"jiayuan-zeng","given":"Jiayuan","family":"Zeng","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"},{"name":"Leilei Sun","slug":"leilei-sun","given":"Leilei","family":"Sun","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"}],"abstract":"Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in traffic forecasting. However, the prediction of origin-destination (OD) demands is still a challenging problem since the number of OD pairs is usually quadratic to the number of stations. In this case, most of the existing spatiotemporal methods fail to handle spatial relations on such a large scale. To address this problem, this paper provides a dynamic graph representation learning framework for OD demands prediction. In particular, a hierarchical memory updater is first proposed to maintain a time-aware representation for each node, and the representations are updated according to the most recently observed OD trips in continuous-time and multiple discrete-time ways. Second, a spatiotemporal propagation mechanism is provided to aggregate representations of neighbor nodes along a random spatiotemporal route which treats origin and destination as two different semantic entities. Last, an objective function is designed to derive the future OD demands according to the most recent node representations, and also to tackle the data sparsity problem in OD prediction. Extensive experiments have been conducted on two real-world datasets, and the experimental results demonstrate the superiority of the proposed method. The code and data are available at https://github.com/Rising0321/HMOD.","doi":"10.24963/ijcai.2022/331","url":"https://ijcai.org/proceedings/2022/331","pdf":"https://ijcai.org/proceedings/2022/0331.pdf","markdown":"https://ijcai.org/proceedings/2022/331.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/331","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":331,"pages":{"first":2383,"last":2389},"keywords":["Data Mining: Mining Graphs","Multidisciplinary Topics and Applications: Transportation","Data Mining: Mining Spatial and/or Temporal Data"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986632","length_sec":88,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984775","length_sec":372,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Chen and Guestrin, 2016] Tianqi Chen and Carlos Guestrin. 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