{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T22:49:37Z","timestamp":1752360577665},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683683","type":"print"},{"value":"9781643683690","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,13]]},"abstract":"<jats:p>Traffic data occupies an important position in intelligent transportation systems (ITS). However, the collected traffic data is often incomplete. We propose a generative adversarial network (GAN) model based on multi-perspective spatiotemporal learning (MST-GAN) to repair data. To achieve the effect of interpolating data from three perspectives: temporal, spatial, and spatiotemporal, we utilize chained generator with independent parameters to progressively refine the learning of temporal and spatial features. In addition, we achieve high-level fusion of multi-perspective features by adversarial between multiple generators and one discriminator. We conduct experiments on two real datasets, showing that the imputation effect of the MST-GAN model is better than other baseline models under different missing patterns. For example, the root mean square error (RMSE) is less than 7.5% and the mean absolute error (MAE) is less than 5% in the random missing scenario, which is much lower than the best performance error of other models.<\/jats:p>","DOI":"10.3233\/faia220519","type":"book-chapter","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T07:58:41Z","timestamp":1671609521000},"source":"Crossref","is-referenced-by-count":2,"title":["Traffic Flow Imputation Based on Multi-Perspective Spatiotemporal Generative Adversarial Networks"],"prefix":"10.3233","author":[{"given":"Guojiang","family":"Shen","sequence":"first","affiliation":[{"name":"College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nali","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghui","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenfeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangjie","family":"Kong","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Proceedings of CECNet 2022"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220519","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T07:58:42Z","timestamp":1671609522000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"ISBN":["9781643683683","9781643683690"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220519","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]}}}