{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T21:05:40Z","timestamp":1776891940326,"version":"3.51.2"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["2023-JC-YB-508"],"award-info":[{"award-number":["2023-JC-YB-508"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neucom.2026.133415","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T15:55:22Z","timestamp":1774108522000},"page":"133415","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["KG-SA-GE: A knowledge graph-enhanced structure-attribute graph embedding for traffic flow forecasting"],"prefix":"10.1016","volume":"682","author":[{"given":"Jiayi","family":"Cao","sequence":"first","affiliation":[]},{"given":"Jianzhong","family":"Chen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.neucom.2026.133415_bib0005","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.neucom.2026.133415_bib0010","series-title":"Proceedings of the 2018 10th International Conference on Machine Learning and Computing","first-page":"26","article-title":"A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data","author":"Agarap","year":"2018"},{"issue":"4","key":"10.1016\/j.neucom.2026.133415_bib0015","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten ZIP code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"10.1016\/j.neucom.2026.133415_bib0020","author":"Kipf"},{"key":"10.1016\/j.neucom.2026.133415_bib0025","unstructured":"A. Singhal, Introducing the knowledge graph: things, not strings (May, 2012), https:\/\/blog.google\/products\/search\/introducing-knowledge-graph-things-not., google Blog."},{"key":"10.1016\/j.neucom.2026.133415_bib0030","author":"Veli\u010dkovi\u0107"},{"issue":"3","key":"10.1016\/j.neucom.2026.133415_bib0035","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s12544-015-0170-8","article-title":"Short-term traffic flow prediction using seasonal arima model with limited input data","volume":"7","author":"Kumar","year":"2015","journal-title":"Eur. Transp. Res. Rev."},{"issue":"6","key":"10.1016\/j.neucom.2026.133415_bib0040","doi-asserted-by":"crossref","first-page":"5231","DOI":"10.1109\/TITS.2021.3052796","article-title":"Short-term traffic flow prediction: an integrated method of econometrics and hybrid deep learning","volume":"23","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133415_bib0045","series-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","first-page":"484","article-title":"Time series forecasting using distribution enhanced linear regression","author":"Ristanoski","year":"2013"},{"key":"10.1016\/j.neucom.2026.133415_bib0050","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.neucom.2018.12.016","article-title":"Traffic flow prediction using LSTM with feature enhancement","volume":"332","author":"Yang","year":"2019","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.neucom.2026.133415_bib0055","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1109\/TITS.2018.2843349","article-title":"An evaluation of HTM and LSTM for short-term arterial traffic flow prediction","volume":"20","author":"Mackenzie","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133415_bib0060","series-title":"2020 12th International Conference on Advanced Computational Intelligence (ICACI)","first-page":"550","article-title":"A short-term traffic flow prediction model based on autoencoder and GRU","author":"Chen","year":"2020"},{"issue":"9","key":"10.1016\/j.neucom.2026.133415_bib0065","doi-asserted-by":"crossref","first-page":"16654","DOI":"10.1109\/TITS.2021.3094659","article-title":"A short-term traffic flow prediction model based on an improved gate recurrent unit neural network","volume":"23","author":"Shu","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"10.1016\/j.neucom.2026.133415_bib0070","first-page":"1688","article-title":"Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning","volume":"15","author":"Zhang","year":"2019","journal-title":"Transp. A: Transp. Sci."},{"key":"10.1016\/j.neucom.2026.133415_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109796","article-title":"A Cnn-bi_lstm parallel network approach for train travel time prediction","volume":"256","author":"Guo","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.neucom.2026.133415_bib0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106041","article-title":"Long-term traffic flow forecasting using a hybrid Cnn-Bilstm model","volume":"121","author":"M\u00e9ndez","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133415_bib0085","author":"Yu"},{"key":"10.1016\/j.neucom.2026.133415_bib0090","author":"Li"},{"issue":"9","key":"10.1016\/j.neucom.2026.133415_bib0095","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-gcn: a temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133415_bib0100","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"922","article-title":"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting","volume":"vol. 33","author":"Guo","year":"2019"},{"key":"10.1016\/j.neucom.2026.133415_bib0105","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"914","article-title":"Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting","volume":"vol. 34","author":"Song","year":"2020"},{"key":"10.1016\/j.neucom.2026.133415_bib0110","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133415_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111637","article-title":"Lsttn: a long-short term transformer-based spatiotemporal neural network for traffic flow forecasting","volume":"293","author":"Luo","year":"2024","journal-title":"Knowl.-Based Syst."},{"issue":"3","key":"10.1016\/j.neucom.2026.133415_bib0120","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1111\/tgis.12644","article-title":"Traffic transformer: capturing the continuity and periodicity of time series for traffic forecasting","volume":"24","author":"Cai","year":"2020","journal-title":"Trans. GIS"},{"key":"10.1016\/j.neucom.2026.133415_bib0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129753","article-title":"Routeformer: transformer utilizing routing mechanism for traffic flow forecasting","volume":"633","author":"Qi","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133415_bib0130","author":"Nie"},{"key":"10.1016\/j.neucom.2026.133415_bib0135","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4365","article-title":"Pdformer: propagation delay-aware dynamic long-range transformer for traffic flow prediction","volume":"vol. 37","author":"Jiang","year":"2023"},{"issue":"2","key":"10.1016\/j.neucom.2026.133415_bib0140","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3589270","article-title":"Lightcts: a lightweight framework for correlated time series forecasting","volume":"1","author":"Lai","year":"2023","journal-title":"Proc. ACM Manag. Data"},{"key":"10.1016\/j.neucom.2026.133415_bib0145","series-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"1567","article-title":"Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting","author":"Shao","year":"2022"},{"key":"10.1016\/j.neucom.2026.133415_bib0150","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4356","article-title":"Spatio-temporal self-supervised learning for traffic flow prediction","volume":"vol. 37","author":"Ji","year":"2023"},{"key":"10.1016\/j.neucom.2026.133415_bib0155","author":"Gao"},{"key":"10.1016\/j.neucom.2026.133415_bib0160","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"3656","article-title":"Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting","volume":"vol. 33","author":"Geng","year":"2019"},{"key":"10.1016\/j.neucom.2026.133415_bib0165","author":"Wu"},{"issue":"9","key":"10.1016\/j.neucom.2026.133415_bib0170","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1080\/19427867.2023.2261706","article-title":"Spatio-temporal graph attention networks for traffic prediction","volume":"16","author":"Ma","year":"2024","journal-title":"Transp. Lett."},{"key":"10.1016\/j.neucom.2026.133415_bib0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119779","article-title":"Multi-view dynamic graph convolution neural network for traffic flow prediction","volume":"222","author":"Huang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133415_bib0180","series-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","first-page":"987","article-title":"Enhancing the robustness via adversarial learning and joint spatial-temporal embeddings in traffic forecasting","author":"Jiang","year":"2023"},{"key":"10.1016\/j.neucom.2026.133415_bib0185","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133415_bib0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122381","article-title":"Dynamic spatial\u2013temporal graph convolutional recurrent networks for traffic flow forecasting","volume":"240","author":"Xia","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"7","key":"10.1016\/j.neucom.2026.133415_bib0195","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TKDE.2017.2718525","article-title":"Road traffic speed prediction: a probabilistic model fusing multi-source data","volume":"30","author":"Lin","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133415_bib0200","doi-asserted-by":"crossref","first-page":"87541","DOI":"10.1109\/ACCESS.2020.2992507","article-title":"Short-term traffic speed prediction of urban road with multi-source data","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133415_bib0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124288","article-title":"Multi-source information fusion graph convolution network for traffic flow prediction","volume":"252","author":"Li","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133415_bib0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129193","article-title":"Multi-factor embedding gnn-based traffic flow prediction considering intersection similarity","volume":"620","author":"Zhong","year":"2025","journal-title":"Neurocomputing"},{"issue":"2","key":"10.1016\/j.neucom.2026.133415_bib0215","first-page":"2133","article-title":"Urban flow pattern mining based on multi-source heterogeneous data fusion and knowledge graph embedding","volume":"35","author":"Liu","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"10.1016\/j.neucom.2026.133415_bib0220","first-page":"1","article-title":"Urbankg: an Urban knowledge graph system","volume":"14","author":"Liu","year":"2023","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.neucom.2026.133415_bib0225","series-title":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs","first-page":"3","article-title":"Developing knowledge graph based system for urban computing","author":"Liu","year":"2022"},{"key":"10.1016\/j.neucom.2026.133415_bib0230","series-title":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","first-page":"1846","article-title":"Towards a knowledge graph-based approach for context-aware points-of-interest recommendations","author":"Halilaj","year":"2021"},{"key":"10.1016\/j.neucom.2026.133415_bib0235","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.inffus.2022.11.018","article-title":"Dual-grained human mobility learning for location-aware trip recommendation with spatial\u2013temporal graph knowledge fusion","volume":"92","author":"Gao","year":"2023","journal-title":"Inf. Fusion."},{"issue":"4","key":"10.1016\/j.neucom.2026.133415_bib0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2023.103369","article-title":"Towards travel recommendation interpretability: disentangling tourist decision-making process via knowledge graph","volume":"60","author":"Gao","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"10.1016\/j.neucom.2026.133415_bib0245","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133415_bib0250","author":"Yang"},{"key":"10.1016\/j.neucom.2026.133415_bib0255","author":"Sun"},{"key":"10.1016\/j.neucom.2026.133415_bib0260","series-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"701","article-title":"Deepwalk: online learning of social representations","author":"Perozzi","year":"2014"},{"key":"10.1016\/j.neucom.2026.133415_bib0265","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"855","article-title":"Node2vec: scalable feature learning for networks","author":"Grover","year":"2016"},{"key":"10.1016\/j.neucom.2026.133415_bib0270","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.neucom.2026.133415_bib0275","author":"Kingma"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122600812X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122600812X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:33:16Z","timestamp":1776889996000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S092523122600812X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":55,"alternative-id":["S092523122600812X"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133415","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"KG-SA-GE: A knowledge graph-enhanced structure-attribute graph embedding for traffic flow forecasting","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133415","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133415"}}