{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T09:06:53Z","timestamp":1765530413208,"version":"3.48.0"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Society of Logistics","award":["2025CSLKT3-083"],"award-info":[{"award-number":["2025CSLKT3-083"]}]},{"name":"China Society of Logistics","award":["2024CSLKT3-089"],"award-info":[{"award-number":["2024CSLKT3-089"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Logistics operations demand real-time visibility and rapid response, yet minute-level traffic speed forecasting remains challenging due to heterogeneous data sources and frequent distribution shifts. This paper proposes a Deep Operator Network (DeepONet)-based framework that treats traffic prediction as learning a mapping from historical states and boundary conditions to future speed states, enabling robust forecasting under changing scenarios. We project logistics demand onto a road network to generate diverse congestion scenarios and employ a branch\u2013trunk architecture to decouple historical dynamics from exogenous contexts. Experiments on both a controlled simulation dataset and the real-world Metropolitan Los Angeles (METR-LA) benchmark demonstrate that the proposed method outperforms classical regression and deep learning baselines in cross-scenario generalization. Specifically, the operator learning approach effectively adapts to unseen boundary conditions without retraining, establishing a promising direction for resilient and adaptive logistics forecasting.<\/jats:p>","DOI":"10.3390\/data10120207","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:30:17Z","timestamp":1765528217000},"page":"207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Operator Learning with Branch\u2013Trunk Factorization for Macroscopic Short-Term Speed Forecasting"],"prefix":"10.3390","volume":"10","author":[{"given":"Bin","family":"Yu","sequence":"first","affiliation":[{"name":"School of Economics and Management, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, China"}]},{"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1875-0262","authenticated-orcid":false,"given":"Dawei","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Digital Economy, Changzhou College of Information Technology, Changzhou 213164, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8872-5169","authenticated-orcid":false,"given":"Joonsoo","family":"Bae","sequence":"additional","affiliation":[{"name":"Department of Industry & Information Systems Engineering, Jeonbuk National University, 567, Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"ref_1","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":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s41019-020-00151-z","article-title":"A Survey of Traffic Prediction: From Spatio-Temporal Data to Intelligent Transportation","volume":"6","author":"Yuan","year":"2021","journal-title":"Data Sci. 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