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However, the ST-ResNet model only extracts the local spatial characteristics and ignores the very important global spatial characteristics. In this paper, a novel Global-Local Spatial-Temporal Residual Correlation Network (GL-STRCN) model is proposed for urban traffic status prediction to further improve the prediction accuracy of the existing ST-ResNet model. The GL-STRCN model firstly applies Pearson\u2019s correlation coefficient method to extract high correlation series. Then, considering both global and local spatial properties, two components consisting of 2D convolution and residual operation are used to capture spatial features. After that, based on Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), a novel long-term temporal feature extraction component is proposed to capture temporal features. Finally, the spatial and temporal features are aggregated together in a weighted way for final prediction. Experiments have also been performed using two datasets from TaxiCD and PEMS-BAY. The results indicated that the proposed model produces a better prediction performance compared with the results based on other baseline solutions, e.g., CNN, ST-ResNet, GL-TCN, and DGLSTNet.<\/jats:p>","DOI":"10.1155\/2022\/7344522","type":"journal-article","created":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T21:35:22Z","timestamp":1643837722000},"page":"1-15","source":"Crossref","is-referenced-by-count":4,"title":["Global-Local Spatial-Temporal Residual Correlation Network for Urban Traffic Status Prediction"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8830-0678","authenticated-orcid":true,"given":"Yin-Xin","family":"Bao","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5519-5197","authenticated-orcid":true,"given":"Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"},{"name":"School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2795-8409","authenticated-orcid":true,"given":"Qin-Qin","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0877-5989","authenticated-orcid":true,"given":"Quan","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"},{"name":"School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.01.005"},{"issue":"1","key":"2","first-page":"37","article-title":"Short term traffic flow prediction methodologies: a review[J]","volume":"2","author":"P. 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