{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:27:21Z","timestamp":1776158841028,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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.\u00a0In 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.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/274","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"1981-1987","source":"Crossref","is-referenced-by-count":224,"title":["STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting"],"prefix":"10.24963","author":[{"given":"Lei","family":"Bai","sequence":"first","affiliation":[{"name":"University of New South Wales"}]},{"given":"Lina","family":"Yao","sequence":"additional","affiliation":[{"name":"University of New South Wales"}]},{"given":"Salil S.","family":"Kanhere","sequence":"additional","affiliation":[{"name":"University of New South Wales"}]},{"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney"}]},{"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[{"name":"Macquarie University"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:48:02Z","timestamp":1564285682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/274"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/274","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}