{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T21:51:26Z","timestamp":1778363486087,"version":"3.51.4"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"CityU Project","award":["7005769"],"award-info":[{"award-number":["7005769"]}]},{"name":"CityU Project","award":["7005895"],"award-info":[{"award-number":["7005895"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tits.2024.3375528","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T18:47:38Z","timestamp":1711046858000},"page":"12297-12314","source":"Crossref","is-referenced-by-count":3,"title":["Visual-Information-Driven Model for Crowd Simulation Using Temporal Convolutional Network"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1680-3409","authenticated-orcid":false,"given":"Xuanwen","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3156-2036","authenticated-orcid":false,"given":"Eric Wai Ming","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/35035023"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2007.04.006"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-28091-X_36"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.51.4282"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2981118"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2010.01.014"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0010047"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.plrev.2016.05.014"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2022.128411"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2016.0414"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2018.06.045"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123825"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2931892"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2542843"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103260"},{"key":"ref16","first-page":"1","article-title":"Modeling analysis of T-shaped crowd flow based on artificial neural network","volume-title":"Proc. CIBDA 3rd Int. Conf. Comput. Inf. Big Data Appl.","author":"Li"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.04.141"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aay0792"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref21","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018","journal-title":"arXiv:1803.01271"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9671488"},{"key":"ref23","volume-title":"Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences","author":"Werbos","year":"1975"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ-IEEE.2012.6251245"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2022.105875"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.80.036110"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1046\/j.1460-9568.2003.02736.x"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2016.10.037"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2010.03.015"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.2009.0405"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.73.1067"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.104100"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3758\/BRM.41.3.957"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.71.036121"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/11861201_57"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.75.046112"},{"key":"ref37","first-page":"1","article-title":"Weight normalization: A simple reparameterization to accelerate training of deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Salimans"},{"key":"ref38","first-page":"1","article-title":"Rectified linear units improve restricted Boltzmann machines","volume-title":"Proc. Icml","author":"Nair"},{"key":"ref39","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/aa620d"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3390\/su11195501"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/ab0c13"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2011\/06\/P06004"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2021.126593"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/aab04f"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1021\/ac60214a047"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref48","article-title":"Social attention: Modeling attention in human crowds","author":"Vemula","year":"2017","journal-title":"arXiv:1710.04689"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1086\/200975"},{"key":"ref50","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2017","journal-title":"arXiv:1412.6980"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2009.12.015"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00135"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref54","article-title":"Scene-LSTM: A model for human trajectory prediction","author":"Manh","year":"2018","journal-title":"arXiv:1808.04018"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/10659279\/10477851.pdf?arnumber=10477851","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T04:06:37Z","timestamp":1725163597000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10477851\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":54,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tits.2024.3375528","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}