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The city government can respond in advance based on gather predictions and greatly reduce the loss and risks caused by vicious gatherings. Compared with other trajectory prediction tasks (i.e., the recommendation of point of interest), gather prediction pay more attention to real\u2010time trajectory data and requests stronger spatial\u2010temporal dependence. At the same time, gather prediction is more focused on scenes with multiple types of trajectories. And the existing methods majorly rely on the trajectory data and ignore the great influence of geographical environment (i.e., road network structure). Therefore, this paper transforms the gather prediction into the trajectory prediction task with strong real\u2010time condition in a certain city and conducts the gathering situations by predicting users\u2019 aggregated movements in next minutes or hours. A novel Spatiotemporal Gate Recurrent Unit (STGRU) model is proposed, where spatiotemporal gates and road network gate are introduced to capture the spatiotemporal relationships between trajectories. Compared with existing methods, we improve the performance of the model by adding road network structure and external knowledges, as well as time and distance gates to reduce model parameters. The proposed STGRU is evaluated on three real\u2010world trajectory datasets, and the experimental results demonstrate the effectiveness of the proposed model.<\/jats:p>","DOI":"10.1155\/2021\/6030144","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T01:50:09Z","timestamp":1626400209000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Road Network Enhanced Gate Recurrent Unit Model for Gather Prediction in Smart Cities"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3436-0226","authenticated-orcid":false,"given":"Mingchao","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8475-9203","authenticated-orcid":false,"given":"Ling","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4834-9549","authenticated-orcid":false,"given":"Ke","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9351-6708","authenticated-orcid":false,"given":"Xu","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2021.9010026"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2019.9020021"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"CaiZ.andHeZ. 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