{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T12:08:38Z","timestamp":1779192518336,"version":"3.51.4"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4300700"],"award-info":[{"award-number":["2022YFB4300700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Fusion"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.inffus.2026.104380","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T15:47:10Z","timestamp":1776354430000},"page":"104380","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Efficient dynamic spatial-focused attention model for spatiotemporal traffic prediction"],"prefix":"10.1016","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2009-5014","authenticated-orcid":false,"given":"Nan","family":"Ouyang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenkang","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Ao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shehui","family":"Bu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojiang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1481-3179","authenticated-orcid":false,"given":"Xin","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6602-7212","authenticated-orcid":false,"given":"Kai","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"7","key":"10.1016\/j.inffus.2026.104380_bib0001","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1145\/2611567","article-title":"Big data and its technical challenges","volume":"57","author":"Jagadish","year":"2014","journal-title":"Commun. ACM"},{"key":"10.1016\/j.inffus.2026.104380_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2025.110313","article-title":"Internet of things-enabled unmanned aerial vehicles for real-time traffic mobility analysis in smart cities","volume":"123","author":"Bakirci","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.inffus.2026.104380_bib0003","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.tra.2020.09.018","article-title":"Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: recent advances and challenges","volume":"141","author":"Outay","year":"2020","journal-title":"Trans. Res. part A Policy Pract."},{"issue":"6","key":"10.1016\/j.inffus.2026.104380_bib0004","doi-asserted-by":"crossref","first-page":"6636","DOI":"10.1109\/TDSC.2025.3588717","article-title":"PPDR: a privacy-preserving dual reputation management scheme in vehicle platoon","volume":"22","author":"Sun","year":"2025","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"10.1016\/j.inffus.2026.104380_bib0005","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","article-title":"Long short-term memory neural network for traffic speed prediction using remote microwave sensor data","volume":"54","author":"Ma","year":"2015","journal-title":"Trans. Res. Part C Emerging Technol."},{"issue":"4","key":"10.1016\/j.inffus.2026.104380_bib0006","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.1007\/s00181-021-02190-5","article-title":"Contemporaneous causality among one hundred Chinese cities","volume":"63","author":"Xu","year":"2022","journal-title":"Empir. Econ."},{"key":"10.1016\/j.inffus.2026.104380_bib0007","article-title":"Urban traffic flow prediction techniques: a review","volume":"35","author":"Medina-Salgado","year":"2022","journal-title":"Sustainable Comput. Inf. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0008","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11121","article-title":"Are transformers effective for time series forecasting?","volume":"37","author":"Zeng","year":"2023"},{"key":"10.1016\/j.inffus.2026.104380_bib0009","series-title":"2019 IEEE International Conference on Big Data (Big Data)","first-page":"3285","article-title":"The performance of LSTM and BiLSTM in forecasting time series","author":"Siami-Namini","year":"2019"},{"issue":"03","key":"10.1016\/j.inffus.2026.104380_bib0010","article-title":"China commodity price index (CCPI) forecasting via the neural network","volume":"12","author":"Jin","year":"2025","journal-title":"Int. J. Financ. Eng."},{"key":"10.1016\/j.inffus.2026.104380_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117921","article-title":"Graph neural network for traffic forecasting: a survey","volume":"207","author":"Jiang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.inffus.2026.104380_bib0012","unstructured":"Y. Li, R. Yu, C. Shahabi, Y. Liu, Diffusion convolutional recurrent neural network: data-driven traffic forecasting, arXiv: 1707.01926 [hep-th](2017)."},{"key":"10.1016\/j.inffus.2026.104380_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101837","article-title":"Integrating the traffic science with representation learning for city-wide network congestion prediction","volume":"99","author":"Zheng","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104380_bib0014","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.24963\/ijcai.2019\/264","article-title":"Graph WaveNet for deep spatial-temporal graph modeling","author":"Wu","year":"2019","journal-title":"IJCAI 2019"},{"issue":"3","key":"10.1016\/j.inffus.2026.104380_bib0015","doi-asserted-by":"crossref","first-page":"4066","DOI":"10.1109\/TITS.2024.3513325","article-title":"Graph transformer-based dynamic edge interaction encoding for traffic prediction","volume":"26","author":"Ouyang","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125474","article-title":"Flow prediction via adaptive dynamic graph with spatio-temporal correlations","volume":"261","author":"Zhang","year":"2025","journal-title":"Expert Syst. Appl. Int. J."},{"key":"10.1016\/j.inffus.2026.104380_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103331","article-title":"Uncertainty-aware traffic accident risk prediction via multi-view hypergraph contrastive learning","volume":"124","author":"Zhang","year":"2025","journal-title":"Inf. Fusion"},{"issue":"11","key":"10.1016\/j.inffus.2026.104380_bib0018","doi-asserted-by":"crossref","first-page":"7169","DOI":"10.1109\/TITS.2020.3002718","article-title":"Dynamic spatial-temporal representation learning for traffic flow prediction","volume":"22","author":"Liu","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0019","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":"33","author":"Guo","year":"2019"},{"issue":"8","key":"10.1016\/j.inffus.2026.104380_bib0020","doi-asserted-by":"crossref","first-page":"8705","DOI":"10.1109\/TITS.2024.3354802","article-title":"A spatiotemporal multiscale graph convolutional network for traffic flow prediction","volume":"25","author":"Cao","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"13","key":"10.1016\/j.inffus.2026.104380_bib0021","doi-asserted-by":"crossref","first-page":"11518","DOI":"10.1109\/JIOT.2023.3243122","article-title":"Spatiotemporal residual graph attention network for traffic flow forecasting","volume":"10","author":"Zhang","year":"2023","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"10.1016\/j.inffus.2026.104380_bib0022","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/TITS.2018.2836141","article-title":"Impact of data loss for prediction of traffic flow on an urban road using neural networks","volume":"20","author":"Pamu\u0142a","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0023","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.trpro.2017.05.006","article-title":"Effect of information availability on stability of traffic flow: percolation theory approach","volume":"23","author":"Talebpour","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"10.1016\/j.inffus.2026.104380_bib0024","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0025","series-title":"International Conference on Learning Representations","first-page":"4004","article-title":"iTransformer: inverted transformers are effective for time series forecasting","author":"Liu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104380_bib0026","first-page":"64145","article-title":"SOFTS: efficient multivariate time series forecasting with series-core fusion","volume":"37","author":"Han","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.inffus.2026.104380_sbref0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113598","article-title":"Dynamic self-attention network based opinion formation over dynamic social networks with application to live-streaming","volume":"183","author":"Mao","year":"2025","journal-title":"Appl. Soft Comput."},{"issue":"19","key":"10.1016\/j.inffus.2026.104380_bib0028","doi-asserted-by":"crossref","first-page":"31467","DOI":"10.1109\/JIOT.2024.3419768","article-title":"GTformer: graph-based temporal-order-aware transformer for long-term series forecasting","volume":"11","author":"Liang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.inffus.2026.104380_bib0029","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.trc.2019.09.008","article-title":"An effective spatial-temporal attention based neural network for traffic flow prediction","volume":"108","author":"Do","year":"2019","journal-title":"Trans. Res. Part C Emerging Technol."},{"key":"10.1016\/j.inffus.2026.104380_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117275","article-title":"A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks","volume":"202","author":"Reza","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.inffus.2026.104380_bib0031","doi-asserted-by":"crossref","unstructured":"L. Bai, L. Yao, S. Kanhere, X. Wang, Q. Sheng, et al., STG2Seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting, arXiv: 1905.10069 [hep-th](2019).","DOI":"10.24963\/ijcai.2019\/274"},{"issue":"9","key":"10.1016\/j.inffus.2026.104380_bib0032","doi-asserted-by":"crossref","first-page":"16612","DOI":"10.1109\/TITS.2021.3113935","article-title":"Attention mechanism with spatial-temporal joint model for traffic flow speed prediction","volume":"23","author":"Hu","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0033","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1109\/ICDE55515.2023.00046","article-title":"When spatio-temporal meet wavelets: disentangled traffic forecasting via efficient spectral graph attention networks","author":"Fang","year":"2023","journal-title":"2023 IEEE 39th Int. Conf. Data Eng. (ICDE)"},{"key":"10.1016\/j.inffus.2026.104380_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102146","article-title":"Traffic flow matrix-based graph neural network with attention mechanism for traffic flow prediction","volume":"104","author":"Chen","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104380_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109575","article-title":"Spatio-temporal attention based collaborative local\u2013global learning for traffic flow prediction","volume":"139","author":"Chi","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"3","key":"10.1016\/j.inffus.2026.104380_bib0036","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/TCYB.2022.3223918","article-title":"MVSTT: a multiview spatial-temporal transformer network for traffic-flow forecasting","volume":"54","author":"Pu","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.inffus.2026.104380_bib0037","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11106","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","volume":"35","author":"Zhou","year":"2021"},{"key":"10.1016\/j.inffus.2026.104380_bib0038","series-title":"International Conference on Learning Representations","first-page":"1060","article-title":"Rethinking attention with performers","author":"Choromanski","year":"2021"},{"key":"10.1016\/j.inffus.2026.104380_bib0039","unstructured":"S. Wang, B.Z. Li, M. Khabsa, H. Fang, H. Ma, Linformer: self-attention with linear complexity, arXiv: 2006.04768 [hep-th](2020)."},{"key":"10.1016\/j.inffus.2026.104380_bib0040","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"14138","article-title":"Nystr\u00f6mformer: a nystr\u00f6m-based algorithm for approximating self-attention","volume":"35","author":"Xiong","year":"2021"},{"key":"10.1016\/j.inffus.2026.104380_bib0041","unstructured":"I. Beltagy, M.E. Peters, A. Cohan, Longformer: the long-document transformer, arXiv: 2004.05150 [hep-th](2020)."},{"key":"10.1016\/j.inffus.2026.104380_bib0042","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.neunet.2023.01.023","article-title":"Interpretable local flow attention for multi-step traffic flow prediction","volume":"161","author":"Huang","year":"2023","journal-title":"Neural Netw."},{"issue":"14","key":"10.1016\/j.inffus.2026.104380_bib0043","doi-asserted-by":"crossref","first-page":"26799","DOI":"10.1109\/JIOT.2025.3561542","article-title":"TWIST: an efficient spatial-temporal transformer with temporal window and sparse attention for traffic forecasting","volume":"12","author":"Wang","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.inffus.2026.104380_bib0044","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123884","article-title":"An efficient spatial-temporal transformer with temporal aggregation and spatial memory for traffic forecasting","volume":"250","author":"Liu","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"10.1016\/j.inffus.2026.104380_bib0045","doi-asserted-by":"crossref","first-page":"4565","DOI":"10.1109\/TITS.2022.3185503","article-title":"Spatial-temporal attention graph convolution network on edge cloud for traffic flow prediction","volume":"24","author":"Lai","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"3","key":"10.1016\/j.inffus.2026.104380_bib0046","doi-asserted-by":"crossref","first-page":"3210","DOI":"10.1109\/TITS.2025.3533560","article-title":"A sparse cross attention-based graph convolution network with auxiliary information awareness for traffic flow prediction","volume":"26","author":"Chen","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0047","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8114","article-title":"Trafformer: unify time and space in traffic prediction","volume":"37","author":"Jin","year":"2023"},{"key":"10.1016\/j.inffus.2026.104380_bib0048","series-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1","first-page":"307","article-title":"Efficient large-scale traffic forecasting with transformers: a spatial data management perspective","author":"Fang","year":"2025"},{"issue":"7","key":"10.1016\/j.inffus.2026.104380_bib0049","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1145\/2611567","article-title":"Big data and its technical challenges","volume":"57","author":"Jagadish","year":"2014","journal-title":"Commun. ACM"},{"issue":"1","key":"10.1016\/j.inffus.2026.104380_bib0050","doi-asserted-by":"crossref","first-page":"96","DOI":"10.3141\/1748-12","article-title":"Freeway performance measurement system: mining loop detector data","volume":"1748","author":"Chen","year":"2001","journal-title":"Transp. Res. Rec."},{"issue":"11","key":"10.1016\/j.inffus.2026.104380_bib0051","doi-asserted-by":"crossref","first-page":"18992","DOI":"10.1109\/TITS.2024.3440650","article-title":"BjTT: a large-scale multimodal dataset for traffic prediction","volume":"25","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0052","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":"34","author":"Song","year":"2020"},{"key":"10.1016\/j.inffus.2026.104380_bib0053","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume":"33","author":"Bai","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0054","series-title":"International Conference on Machine Learning","first-page":"11906","article-title":"DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting","author":"Lan","year":"2022"},{"key":"10.1016\/j.inffus.2026.104380_bib0055","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":"37","author":"Jiang","year":"2023"},{"key":"10.1016\/j.inffus.2026.104380_bib0056","series-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","first-page":"4125","article-title":"Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting","author":"Liu","year":"2023"},{"key":"10.1016\/j.inffus.2026.104380_bib0057","series-title":"The Twelfth International Conference on Learning Representations","article-title":"TESTAM: a time-enhanced spatio-temporal attention model with mixture of experts","author":"Lee","year":"2024"},{"issue":"4","key":"10.1016\/j.inffus.2026.104380_bib0058","doi-asserted-by":"crossref","first-page":"4770","DOI":"10.1109\/TITS.2025.3537637","article-title":"A task-oriented spatial graph structure learning method for traffic forecasting","volume":"26","author":"Wang","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.inffus.2026.104380_bib0059","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"753","article-title":"Connecting the dots: multivariate time series forecasting with graph neural networks","author":"Wu","year":"2020"},{"key":"10.1016\/j.inffus.2026.104380_bib0060","series-title":"Proceedings of International Conference on Learning Representations","article-title":"Discrete graph structure learning for forecasting multiple time series","author":"Shang","year":"2021"},{"key":"10.1016\/j.inffus.2026.104380_bib0061","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"7218","article-title":"Scalable spatiotemporal graph neural networks","volume":"37","author":"Cini","year":"2023"},{"key":"10.1016\/j.inffus.2026.104380_bib0062","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"1093","article-title":"Efficient traffic prediction through spatio-temporal distillation","volume":"39","author":"Zhang","year":"2025"},{"issue":"3","key":"10.1016\/j.inffus.2026.104380_bib0063","doi-asserted-by":"crossref","first-page":"3210","DOI":"10.1109\/TITS.2025.3533560","article-title":"A sparse cross attention-based graph convolution network with auxiliary information awareness for traffic flow prediction","volume":"26","author":"Chen","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["Information Fusion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526002599?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526002599?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T11:43:36Z","timestamp":1779191016000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1566253526002599"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":63,"alternative-id":["S1566253526002599"],"URL":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104380","relation":{},"ISSN":["1566-2535"],"issn-type":[{"value":"1566-2535","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Efficient dynamic spatial-focused attention model for spatiotemporal traffic prediction","name":"articletitle","label":"Article Title"},{"value":"Information Fusion","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104380","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":"104380"}}