{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:20:10Z","timestamp":1773793210834,"version":"3.50.1"},"reference-count":39,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3665240","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T21:08:09Z","timestamp":1771276089000},"page":"28994-29017","source":"Crossref","is-referenced-by-count":0,"title":["A Four-Phase Spatio-Temporal and Quantum-Enhanced Framework for Traffic Speed Forecasting With Calibrated Uncertainty"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1452-4502","authenticated-orcid":false,"given":"Shishir Singh","family":"Chauhan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7310-1177","authenticated-orcid":false,"given":"Vijay Kumar","family":"Jha","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1573-1853","authenticated-orcid":false,"given":"Gauri Shanker","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.01.005"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2345663"},{"key":"ref3","first-page":"2216","article-title":"Graph neural networks in traffic forecasting: A survey","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.2.270"},{"key":"ref5","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"key":"ref6","first-page":"174665","article-title":"Deep learning for spatio-temporal traffic state estimation: A survey","volume":"8","author":"Yu","year":"2020","journal-title":"IEEE Access"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref8","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Li"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"issue":"5","key":"ref12","first-page":"4649","article-title":"ST-transformer: Spatial\u2013temporal transformer for power grid traffic forecasting","volume":"36","author":"Xu","year":"2021","journal-title":"IEEE Trans. Power Syst."},{"key":"ref13","first-page":"3634","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","volume-title":"Proc. 27th Int. Joint Conf. Artif. Intell.","author":"Yu"},{"issue":"1","key":"ref14","first-page":"914","article-title":"Spatial-temporal synchronous graph convolutional networks: A new framework for Spatial\u2013temporal network data forecasting","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"34","author":"Song"},{"key":"ref15","first-page":"2875","article-title":"Diffusion convolutional recurrent neural network revisited: A deep generative approach for traffic forecasting","volume-title":"Proc. 30th Int. Joint Conf. Artif. Intell. (IJCAI)","author":"Deng"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"key":"ref17","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Lakshminarayanan"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2307\/1913643"},{"issue":"143","key":"ref19","first-page":"1","article-title":"Prediction intervals via conformal inference","volume":"21","author":"Angelopoulos","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-06649-8"},{"key":"ref21","article-title":"Classification methods based on conformal prediction","author":"Lei","year":"2013"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2003.02989"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.98.032309"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0980-2"},{"key":"ref25","article-title":"A real-time traffic dataset for its: Metr-la","author":"Chen","year":"2015"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/65.2.297"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1115\/1.3662552"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-52452-8"},{"issue":"1","key":"ref29","article-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"32","author":"Yan"},{"key":"ref30","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume-title":"Proc. 34th Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Bai"},{"key":"ref31","article-title":"Spatial\u2013temporal transformer networks for traffic flow forecasting","author":"Xu","year":"2020","journal-title":"arXiv:2001.02908"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/130385.130401"},{"key":"ref33","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Sutskever"},{"key":"ref34","first-page":"1234","article-title":"Spatio-temporal graph attention networks for traffic forecasting","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Guo"},{"key":"ref35","volume-title":"Forecasting: Principles and Practice","author":"Hyndman","year":"2021"},{"key":"ref36","article-title":"Spatio-temporal transformer networks for traffic flow forecasting","author":"Xu","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref37","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"Proc. ICLR","author":"Song"},{"key":"ref38","volume-title":"Real-time traffic management: System requirements and operational constraints","author":"Department of Transportation","year":"2017"},{"key":"ref39","volume-title":"The NIST definition of edge computing and latency requirements","year":"2020"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11397327.pdf?arnumber=11397327","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:24:46Z","timestamp":1773779086000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11397327\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3665240","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}