{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T03:41:44Z","timestamp":1786246904506,"version":"build-2736575974"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"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"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.eswa.2026.133896","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T23:28:06Z","timestamp":1785799686000},"page":"133896","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["DuGTRL: Dual-view grid-based trajectory representation learning framework integrating spatiotemporal semantics"],"prefix":"10.1016","volume":"333","author":[{"given":"Yamei","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5816-3190","authenticated-orcid":false,"given":"Qingying","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0296-8192","authenticated-orcid":false,"given":"Chuanming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonglong","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.133896_bib0001","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"11463","article-title":"Spatiotemporal-aware trend-seasonality decomposition network for traffic flow forecasting","volume":"vol. 39","author":"Cao","year":"2025"},{"key":"10.1016\/j.eswa.2026.133896_bib0002","series-title":"Proceedings of the IEEE 39th international conference on data engineering (ICDE)","first-page":"2933","article-title":"Contrastive trajectory similarity learning with dual-feature attention","author":"Chang","year":"2023"},{"key":"10.1016\/j.eswa.2026.133896_bib0003","unstructured":"Chen, W., Liang, Y., Zhu, Y., Chang, Y., Luo, K., Wen, H., Li, L., Yu, Y., Wen, Q., Chen, C. et al. (2024). Deep learning for trajectory data management and mining: A survey and beyond. arXiv preprint arXiv: 2403.14151."},{"key":"10.1016\/j.eswa.2026.133896_bib0004","series-title":"Proceedings of the 30th ACM international conference on information & knowledge management","first-page":"211","article-title":"Robust road network representation learning: When traffic patterns meet traveling semantics","author":"Chen","year":"2021"},{"key":"10.1016\/j.eswa.2026.133896_bib0005","series-title":"Proceedings of the conference of the north american chapter of the association for computational linguistics: Human language technologies","first-page":"4171","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"key":"10.1016\/j.eswa.2026.133896_bib0006","series-title":"Proceedings of the IEEE 37th international conference on data engineering (ICDE)","first-page":"696","article-title":"E2DTC: An end to end deep trajectory clustering framework via self-training","author":"Fang","year":"2021"},{"key":"10.1016\/j.eswa.2026.133896_bib0007","series-title":"Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"347","article-title":"Spatio-temporal trajectory similarity learning in road networks","author":"Fang","year":"2022"},{"issue":"1","key":"10.1016\/j.eswa.2026.133896_bib0008","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3361741","article-title":"Trembr: Exploring road networks for trajectory representation learning","volume":"11","author":"Fu","year":"2020","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"10","key":"10.1016\/j.eswa.2026.133896_bib0009","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"Lstm: A search space odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2026.133896_bib0010","series-title":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","first-page":"855","article-title":"Node2vec: Scalable feature learning for networks","author":"Grover","year":"2016"},{"issue":"12","key":"10.1016\/j.eswa.2026.133896_bib0011","doi-asserted-by":"crossref","first-page":"9052","DOI":"10.1109\/TPAMI.2024.3415112","article-title":"A survey on self-supervised learning: Algorithms, applications, and future trends","volume":"46","author":"Gui","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"15","key":"10.1016\/j.eswa.2026.133896_bib0012","doi-asserted-by":"crossref","first-page":"29642","DOI":"10.1109\/JIOT.2025.3568781","article-title":"Auto-GAN: GAN-based self-supervised collaborative learning for robust spatio-temporal trajectory classification in iot","volume":"12","author":"Jia","year":"2025","journal-title":"IEEE Internet of Things Journal"},{"key":"10.1016\/j.eswa.2026.133896_bib0013","series-title":"Proceedings of the IEEE 39th international conference on data engineering (ICDE)","first-page":"843","article-title":"Self-supervised trajectory representation learning with temporal regularities and travel semantics","author":"Jiang","year":"2023"},{"key":"10.1016\/j.eswa.2026.133896_bib0014","series-title":"Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"1188","article-title":"Mm-dag: Multi-task dag learning for multi-modal data-with application for traffic congestion analysis","author":"Lan","year":"2023"},{"issue":"4","key":"10.1016\/j.eswa.2026.133896_bib0015","doi-asserted-by":"crossref","first-page":"6752","DOI":"10.1109\/TNNLS.2024.3386810","article-title":"Dual-channel adaptive scale hypergraph encoders with cross-view contrastive learning for knowledge tracing","volume":"36","author":"Li","year":"2025","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2026.133896_bib0016","series-title":"Proceedings of IEEE 34th international conference on data engineering (ICDE)","first-page":"617","article-title":"Deep representation learning for trajectory similarity computation","author":"Li","year":"2018"},{"issue":"12","key":"10.1016\/j.eswa.2026.133896_bib0017","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2022","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2026.133896_bib0018","series-title":"Proceedings of the 31st ACM international conference on information & knowledge management","first-page":"1229","article-title":"Trajformer: Efficient trajectory classification with transformers","author":"Liang","year":"2022"},{"key":"10.1016\/j.eswa.2026.133896_bib0019","unstructured":"Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv: 1506.00019."},{"key":"10.1016\/j.eswa.2026.133896_bib0020","series-title":"Proceedings of the ACM web conference","first-page":"3064","article-title":"More than routing: Joint GPS and route modeling for refine trajectory representation learning","author":"Ma","year":"2024"},{"key":"10.1016\/j.eswa.2026.133896_bib0021","series-title":"Proceedings of the 31st ACM international conference on information & knowledge management","first-page":"1501","article-title":"Jointly contrastive representation learning on road network and trajectory","author":"Mao","year":"2022"},{"key":"10.1016\/j.eswa.2026.133896_bib0022","series-title":"Proceedings of the 27th international conference on machine learning","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","author":"Nair","year":"2010"},{"key":"10.1016\/j.eswa.2026.133896_bib0023","series-title":"Proceedings of the IEEE 40th international conference on data engineering (ICDE)","first-page":"1282","article-title":"Muse-net: Disentangling multi-periodicity for traffic flow forecasting","author":"Qin","year":"2024"},{"key":"10.1016\/j.eswa.2026.133896_bib0024","series-title":"Proceedings of the advances in neural information processing systems","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"Sutskever","year":"2014"},{"issue":"11","key":"10.1016\/j.eswa.2026.133896_bib0025","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.133896_bib0026","series-title":"Proceedings of the 31st international conference on neural information processing systems (neurIPS)","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.eswa.2026.133896_bib0027","series-title":"Proceedings of the international conference on learning representations","first-page":"1","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2018"},{"issue":"7","key":"10.1016\/j.eswa.2026.133896_bib0028","doi-asserted-by":"crossref","first-page":"7687","DOI":"10.1109\/TITS.2024.3350339","article-title":"A deep spatiotemporal trajectory representation learning framework for clustering","volume":"25","author":"Wang","year":"2024","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.eswa.2026.133896_bib0029","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"2500","article-title":"When will you arrive? estimating travel time based on deep neural networks","volume":"vol. 32","author":"Wang","year":"2018"},{"key":"10.1016\/j.eswa.2026.133896_bib0030","series-title":"Proceedings of the 29th international conference on advances in geographic information systems","first-page":"145","article-title":"Libcity: An open library for traffic prediction","author":"Wang","year":"2021"},{"key":"10.1016\/j.eswa.2026.133896_bib0031","doi-asserted-by":"crossref","first-page":"22099","DOI":"10.52202\/075280-0970","article-title":"Connecting multi-modal contrastive representations","volume":"36","author":"Wang","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.1016\/j.eswa.2026.133896_bib0032","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"10.1016\/j.eswa.2026.133896_bib0033","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1080\/13658816.2017.1400548","article-title":"Fast map matching, an algorithm integrating hidden markov model with precomputation","volume":"32","author":"Yang","year":"2018","journal-title":"International Journal of Geographical Information Science"},{"key":"10.1016\/j.eswa.2026.133896_bib0034","series-title":"Proceedings of the IEEE 37th international conference on data engineering (ICDE)","first-page":"2183","article-title":"T3s: Effective representation learning for trajectory similarity computation","author":"Yang","year":"2021"},{"key":"10.1016\/j.eswa.2026.133896_bib0035","series-title":"Proceedings of the 30th international joint conference on artificial intelligence","first-page":"3286","article-title":"Unsupervised path representation learning with curriculum negative sampling","author":"Yang","year":"2021"},{"key":"10.1016\/j.eswa.2026.133896_bib0036","series-title":"Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"2275","article-title":"Trajgat: A graph-based long-term dependency modeling approach for trajectory similarity computation","author":"Yao","year":"2022"},{"key":"10.1016\/j.eswa.2026.133896_bib0037","series-title":"2017 International joint conference on neural networks (IJCNN)","first-page":"3880","article-title":"Trajectory clustering via deep representation learning","author":"Yao","year":"2017"},{"issue":"1","key":"10.1016\/j.eswa.2026.133896_bib0038","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3709721","article-title":"Rler-tte: An efficient and effective framework for en route travel time estimation with reinforcement learning","volume":"3","author":"Zheng","year":"2025","journal-title":"Proceedings of the ACM on Management of Data"},{"key":"10.1016\/j.eswa.2026.133896_bib0039","series-title":"Proceedings of the 31st ACM SIGKDD conference on knowledge discovery and data mining","first-page":"2135","article-title":"Grid and road expressions are complementary for trajectory representation learning","author":"Zhou","year":"2025"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426028046?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426028046?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T02:53:50Z","timestamp":1786244030000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426028046"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":39,"alternative-id":["S0957417426028046"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133896","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DuGTRL: Dual-view grid-based trajectory representation learning framework integrating spatiotemporal semantics","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133896","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133896"}}