{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:37:51Z","timestamp":1777696671506,"version":"3.51.4"},"reference-count":40,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2024,9,19]]},"abstract":"<jats:p>Traffic forecasting has become a core component of Intelligent Transportation Systems. However, accurate traffic forecasting is very challenging, caused by the complex traffic road networks. Most existing forecasting methods do not fully consider the topological structure information of road networks, making it difficult to extract accurate spatial features. In addition, spatial and temporal features have different impacts on traffic conditions, but the existing studies ignore the distribution of spatial-temporal features in traffic regions. To address these limitations, we propose a novel graph neural network architecture named Attention-based Spatial-Temporal Adaptive Integration Gated Network (AST-AIGN). The originality of AST-AIGN is to obtain a spatial feature that more accurately reflects the topological structure of the road networks by embedding Graph Attention Network (GAT) into Jumping Knowledge Net (JK-Net). We propose a data-dependent function called spatial-temporal adaptive integration gate to process the diversity of feature distribution and highlight features in road networks that significantly affects traffic conditions. We evaluate our model on two real-world traffic datasets from the Caltrans Performance Measurement System (PEMS04 and PEMS08), and the extensive experimental results demonstrate the proposed AST-AIGN architecture outperforms other baselines.<\/jats:p>","DOI":"10.3233\/ida-230101","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T10:45:09Z","timestamp":1706870709000},"page":"1245-1269","source":"Crossref","is-referenced-by-count":1,"title":["Combining jumping knowledge into traffic forecasting: An attention-based spatial-temporal adaptive integration gated network"],"prefix":"10.1177","volume":"28","author":[{"given":"Rucheng","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer Science, China University of Geosciences, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongmei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science, China University of Geosciences, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiabao","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer Science, China University of Geosciences, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geyong","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/IDA-230101_ref1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/23249935.2014.932469","article-title":"A review of travel time estimation and forecasting for advanced traveller information systems","volume":"11","author":"Mori","year":"2015","journal-title":"Transportmetrica A: Transport Science"},{"issue":"14","key":"10.3233\/IDA-230101_ref2","first-page":"4501","article-title":"Research on short-term traffic flow prediction based on microcosmic simulation","volume":"21","author":"Ma","year":"2009","journal-title":"Journal of System Simulation"},{"issue":"S1","key":"10.3233\/IDA-230101_ref3","first-page":"101","article-title":"Application of quadric exponential smoothing model in short-term prediction of traffic information","volume":"28","author":"Xueli","year":"2011","journal-title":"Journal of Highway and Transportation Research and Development"},{"issue":"1","key":"10.3233\/IDA-230101_ref4","first-page":"22","article-title":"Short-term traffic and travel time prediction models","volume":"22","author":"Van Lint","year":"2012","journal-title":"Artificial Intelligence Applications to Critical Transportation Issues"},{"issue":"5","key":"10.3233\/IDA-230101_ref5","first-page":"64","article-title":"A short-term traffic flow forecast algorithm based on double seasonal time series","volume":"45","author":"Qiu","year":"2013","journal-title":"Journal of Sichuan University (Engineering Science Edition)"},{"issue":"9","key":"10.3233\/IDA-230101_ref6","first-page":"71","article-title":"Short-term traffic flow forecasting model based on support vector machine regression","volume":"41","author":"Fu","year":"2013","journal-title":"Journal of South China University of Technology (Natural Science Edition)"},{"issue":"3","key":"10.3233\/IDA-230101_ref7","first-page":"322","article-title":"Bayesian network model for traffic flow estimation using prior link flows","volume":"29","author":"Lin","year":"2013","journal-title":"Journal of Southeast University (English Edition)"},{"key":"10.3233\/IDA-230101_ref9","doi-asserted-by":"crossref","unstructured":"Y. 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