{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T06:09:22Z","timestamp":1773468562902,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Natural Resources","award":["LMEE-KF2024011"],"award-info":[{"award-number":["LMEE-KF2024011"]}]},{"name":"Chongqing Planning and Natural Resources Bureau","award":["KJ-2025016"],"award-info":[{"award-number":["KJ-2025016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Travel time prediction (TTP) is a fundamental pillar of intelligent transportation systems (ITS). However, deploying highly parameterized deep learning models in data-scarce environments\u2014referred to as the \u201ccold-start\u201d problem\u2014remains a critical bottleneck, frequently leading to overfitting and severe error accumulation on ultra-long trajectories. To surmount these limitations, this study proposes the Dual-Attentional Gated Residual Network (DAGRN), a data-efficient forecasting framework driven by a novel topology-temporal coordination mechanism. Specifically, the framework introduces three integrated innovations: (1) transforming the primal network into a physics-aware Line Graph to explicitly filter out illegal movements and dynamically modulating topological propagation via Feature-wise Linear Modulation (FiLM); (2) coupling a Bidirectional GRU backbone with a Multi-Head Attention module to simultaneously capture global trends and localized intersection delays; (3) employing a Gated Residual Fusion mechanism that preserves dimensional consistency and facilitates gradient flow in extensive sequences. To rigorously validate the model\u2019s robustness, we conduct evaluations on a highly constrained, stratified dataset comprising merely 2000 trajectories. Experimental results demonstrate that DAGRN achieves state-of-the-art predictive precision with an RMSE of 415.485 s and an R2 of 0.848, significantly outperforming 12 advanced baseline models and reducing error by up to 13.8% against the strongest graph baseline. Comprehensive ablation studies confirm the absolute necessity of the Multi-Head Attention module, whose removal causes the most severe performance degradation (RMSE surging to 521.495 s). Ultimately, DAGRN presents a readily deployable solution for sparse-data ITS regimes, actively paving the way for future hybrid integrations with microscopic traffic simulations and evolutionary road network optimization algorithms.<\/jats:p>","DOI":"10.3390\/ijgi15030120","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T13:12:24Z","timestamp":1773321144000},"page":"120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Dual-Attentional Gated Residual Framework for Robust Travel Time Prediction"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0593-9474","authenticated-orcid":false,"given":"Jiajun","family":"Wu","sequence":"first","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"},{"name":"Chongqing Key Laboratory of Spatial-Temporal Information for Mountain Cities, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6506-4263","authenticated-orcid":false,"given":"Yongchuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"},{"name":"Chongqing Key Laboratory of Spatial-Temporal Information for Mountain Cities, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiduo","family":"Bai","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Spatial-Temporal Information for Mountain Cities, Chongqing 400074, China"},{"name":"Chongqing Academy of Surveying and Mapping, Chongqing 401121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xia","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Spatial-Temporal Information for Mountain Cities, Chongqing 400074, China"},{"name":"Chongqing Academy of Surveying and Mapping, Chongqing 401121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"},{"name":"Chongqing Key Laboratory of Spatial-Temporal Information for Mountain Cities, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Billings, D., and Yang, J.-S. 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