{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T19:17:14Z","timestamp":1791487034011,"version":"4.3.4"},"reference-count":12,"publisher":"World Scientific Pub Co Pte Ltd","issue":"08","funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["61762092"],"award-info":[{"award-number":["61762092"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province","award":["2017SE204"],"award-info":[{"award-number":["2017SE204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p> Traffic flow prediction has great significance for improving road traffic capacity and traffic safety. However, traffic flow in a certain area is usually affected by some factors such as weather, holidays and neighboring areas. So, traffic situation is complicated and traffic flow prediction is difficult. How to use existing traffic data information to predict future traffic flow is the key to this problem. In this paper, we develop an accurate prediction model based on dilated convolution \u2014 ST-MINet (Deep Spatio-Temporal Modified-Inception with Dilated convolution Networks). We fully consider the complexity, nonlinearity and uncertainly of traffic network by summarizing various network models such as ResNet and Inception. So, we use the deep space-time residual network to ensure the convolution accuracy of the information\u2019s position distribution on the basis of existing networks. Then, we add the cavity convolution to the model, which can effectively control the field of view of the convolution kernel. In the experimental part, we compare ten classical algorithms with our ST-MINet, it shows that our model has higher accuracy than others. <\/jats:p>","DOI":"10.1142\/s0218001420520035","type":"journal-article","created":{"date-parts":[[2019,9,6]],"date-time":"2019-09-06T03:46:36Z","timestamp":1567741596000},"page":"2052003","source":"Crossref","is-referenced-by-count":8,"title":["Deep Spatio-Temporal Modified-Inception with Dilated Convolution Networks for Citywide Crowd Flows Prediction"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2807-1233","authenticated-orcid":false,"given":"Yan","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Software Engineering, School of Software, Yunnan University, Kunming, P. R. 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2019,11,29]]},"reference":[{"key":"S0218001420520035BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOMW.2017.8116535"},{"key":"S0218001420520035BIB004","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"S0218001420520035BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/DDCLS.2017.8068141"},{"key":"S0218001420520035BIB007","doi-asserted-by":"publisher","DOI":"10.1080\/21680566.2017.1354737"},{"key":"S0218001420520035BIB008","doi-asserted-by":"publisher","DOI":"10.1111\/j.1553-2712.1998.tb02493.x"},{"key":"S0218001420520035BIB009","doi-asserted-by":"publisher","DOI":"10.1145\/1463434.1463490"},{"key":"S0218001420520035BIB011","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"S0218001420520035BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.08.067"},{"key":"S0218001420520035BIB013","doi-asserted-by":"publisher","DOI":"10.1115\/1.2944238"},{"key":"S0218001420520035BIB015","first-page":"1655","volume-title":"Thirty-First AAAI Conference on Artificial Intelligence","author":"Zhang J.","year":"2017"},{"key":"S0218001420520035BIB016","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2997016"},{"issue":"3","key":"S0218001420520035BIB017","first-page":"1","volume":"5","author":"Zheng Y.","year":"2014","journal-title":"Acm Transac. Intell. Syst. 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