{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T10:29:41Z","timestamp":1785148181167,"version":"3.55.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61602354, 61876138"],"award-info":[{"award-number":["61602354, 61876138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"crossref","award":["2019JM-227"],"award-info":[{"award-number":["2019JM-227"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi\u2019an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.<\/jats:p>","DOI":"10.1145\/3510829","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T13:17:22Z","timestamp":1641907042000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction"],"prefix":"10.1145","volume":"13","author":[{"given":"He","family":"Li","sequence":"first","affiliation":[{"name":"Xidian University, Xi\u2019an, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejiao","family":"Li","sequence":"additional","affiliation":[{"name":"Xidian University, Xi\u2019an, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangcai","family":"Su","sequence":"additional","affiliation":[{"name":"Xidian University, Xi\u2019an, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duo","family":"Jin","sequence":"additional","affiliation":[{"name":"Xidian University, Xi\u2019an, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianbin","family":"Huang","sequence":"additional","affiliation":[{"name":"Xidian University, Xi\u2019an, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deshuang","family":"Huang","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, Guangxi Zhuang Autonomous Region, Nanning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/3367243.3367314"},{"key":"e_1_3_2_3_2","unstructured":"Lei Bai Lina Yao Can Li Xianzhi Wang and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. In Advances in Neural Information Processing Systems H. Larochelle M. Ranzato R. Hadsell M. F. Balcan and H. Lin (Eds.) Vol. 33. Curran Associates Inc. 17804\u201317815. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/ce1aad92b939420fc17005e5461e6f48-Paper.pdf."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5758"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.5555\/3367243.3367357"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"e_1_3_2_7_2","article-title":"Multi-modal graph interaction for multi-graph convolution network in urban spatiotemporal forecasting","author":"Geng Xu","year":"2019","unstructured":"Xu Geng, Xiyu Wu, Lingyu Zhang, Qiang Yang, Yan Liu, and Jieping Ye. 2019. Multi-modal graph interaction for multi-graph convolution network in urban spatiotemporal forecasting. arXiv:1905.11395. 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