{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:51:01Z","timestamp":1775541061967,"version":"3.50.1"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Beijing Municipal Natural Science Foundation","award":["4192020"],"award-info":[{"award-number":["4192020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Netw. Sci. Eng."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tnse.2026.3674916","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:24:56Z","timestamp":1773779096000},"page":"7914-7930","source":"Crossref","is-referenced-by-count":0,"title":["Spatio-Temporal Heritable Neural Networks for Traffic Flow Prediction"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9982-5488","authenticated-orcid":false,"given":"Weilong","family":"Ding","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2870-4037","authenticated-orcid":false,"given":"Qi","family":"Yu","sequence":"additional","affiliation":[{"name":"PipeChina Digital Company, Ltd., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7146-8829","authenticated-orcid":false,"given":"Tianpu","family":"Zhang","sequence":"additional","affiliation":[{"name":"SINOPEC Beijing Research Institute of Chemical Industry, China Petroleum and Chemical Corporation (Sinopec Corp), Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9044-6578","authenticated-orcid":false,"given":"Yuwei","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5769-9540","authenticated-orcid":false,"given":"Chaofan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1263-9121","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7869-6373","authenticated-orcid":false,"given":"Ahmad Taher","family":"Azar","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6861-9684","authenticated-orcid":false,"given":"Honghao","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","volume-title":"Proc. NIPS 2014 Workshop Deep Learn.","author":"Chung","year":"2014"},{"key":"ref2","article-title":"Spectral networks and locally connected networks on graphs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bruna","year":"2014"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3126830"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.108529","article-title":"Adaptive dynamic spatial-temporal graph convolutional neural network for traffic flow prediction","volume-title":"Neural Netw.","author":"Jiang","year":"2026"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/INNOVATIONS.2016.7880022"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/9354273"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CAC.2017.8243253"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2004.837813"},{"key":"ref9","first-page":"481","article-title":"Co-Star: A collaborative prediction service for short-term trends on continuous spatio-temporal data","volume-title":"Future Gener. Comput. Syst.","volume":"102","author":"Ding","year":"2020"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2006.869623"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9876.2012.01059.x"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1186\/s13677-020-00170-1"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCIS.2010.70"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3141\/1811-04"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"ref16","first-page":"12389","article-title":"WitRAN: Water-wave information transmission and recurrent acceleration network for long-range time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Jia","year":"2023"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3647705"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i11.33303"},{"issue":"1","key":"ref19","article-title":"WaveNet: A generative model for raw audio","volume":"12","author":"Den","year":"2016"},{"key":"ref20","first-page":"1","article-title":"ModernTCN: A modern pure convolution structure for general time series analysis","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Luo","year":"2024"},{"key":"ref21","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Wu","year":"2023"},{"key":"ref22","article-title":"MICN: Multi-scale local and global context modeling for long-term series forecasting","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Wang","year":"2023"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3701716.3715214"},{"key":"ref24","article-title":"TVNet: A novel time series analysis method based on dynamic convolution and 3D-variation","volume-title":"Proc. 13th Int. Conf. Learn. Representations","author":"Li","year":"2025"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref26","article-title":"Long-term forecasting with tide: Time-series dense encoder","volume-title":"Trans. Mach. Learn. Res.","author":"Das","year":"2023"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.52202\/075280-3349"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0538"},{"key":"ref29","article-title":"TimeMixer: Decomposable multiscale mixing for time series forecasting","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Wang","year":"2024"},{"issue":"12","key":"ref30","first-page":"12640","article-title":"Unlocking the power of patch: Patch-based MLP for long-term time series forecasting","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"39","author":"Tang","year":"2025"},{"key":"ref31","article-title":"Is Mamba effective for time series forecasting?","volume-title":"Neurocomputing","volume":"619","author":"Wang","year":"2025"},{"key":"ref32","article-title":"Bi-Mamba : Bidirectional mamba for time series forecasting","author":"Liang","year":"2024"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i20.35463"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3737119"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12644"},{"issue":"4","key":"ref36","first-page":"4365","article-title":"PDFormer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"37","author":"Jiang","year":"2023"},{"key":"ref37","first-page":"22419","article-title":"AutoFormer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1201\/9781003612742-2"},{"key":"ref40","article-title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu","year":"2022"},{"key":"ref41","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Zhang","year":"2023"},{"key":"ref42","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Nie","year":"2023"},{"key":"ref43","article-title":"CARD: Channel aligned robust blend transformer for time series forecasting","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Wang","year":"2024"},{"key":"ref44","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Velikovi","year":"2018"},{"key":"ref45","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Hamilton","year":"2017"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3470889"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-025-05123-4"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.28991"},{"key":"ref49","article-title":"Graph-based time series clustering for end-to-end hierarchical forecasting","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Cini","year":"2024"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599418"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599510"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29500"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28707"},{"issue":"01","key":"ref54","first-page":"922","article-title":"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"33","author":"Guo","year":"2019"},{"key":"ref55","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf","year":"2017"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3219618"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref58","first-page":"233","article-title":"Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction","volume-title":"Neural Netw.","volume":"145","author":"Ali","year":"2022"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10486-4"},{"issue":"5","key":"ref60","first-page":"4189","article-title":"Spatial-temporal fusion graph neural networks for traffic flow forecasting","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"35","author":"Li","year":"2021"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/545"},{"key":"ref62","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2018"},{"key":"ref63","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"BAI","year":"2020"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3532611"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2025.3525848"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2025.3579617"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i11.33281"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641217"},{"key":"ref69","article-title":"Make graph neural networks great again: A generic integration paradigm of topology-free patterns for traffic speed prediction","volume-title":"Proc. 33rd Int. Joint Conf. Artif. Intell.","author":"Zhou","year":"2024"},{"key":"ref70","first-page":"852","article-title":"Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks","volume-title":"Inf. Sci.","volume":"577","author":"Ali","year":"2021"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3056926"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3403415"},{"key":"ref73","article-title":"Irregular multivariate time series forecasting: A transformable patching graph neural networks approach","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Zhang","year":"2024"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3583391"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/tcss.2025.3588266"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.3215"},{"key":"ref77","article-title":"Scheduled sampling for sequence prediction with recurrent neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Bengio","year":"2015"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"issue":"01","key":"ref79","first-page":"914","article-title":"Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"34","author":"Song","year":"2020"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2023.123033"}],"container-title":["IEEE Transactions on Network Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488902\/11264281\/11437526.pdf?arnumber=11437526","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T04:56:27Z","timestamp":1775537787000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11437526\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":80,"URL":"https:\/\/doi.org\/10.1109\/tnse.2026.3674916","relation":{},"ISSN":["2327-4697","2334-329X"],"issn-type":[{"value":"2327-4697","type":"electronic"},{"value":"2334-329X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}