{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T12:03:35Z","timestamp":1781006615500,"version":"3.54.1"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116109","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T22:55:07Z","timestamp":1777762507000},"page":"116109","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["STC-Traj2vec: Spatiotemporal context-integrated trajectory representation learning for clustering"],"prefix":"10.1016","volume":"345","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8647-4789","authenticated-orcid":false,"given":"Aparna","family":"Raveendran","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumam Mary","family":"Idicula","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.knosys.2026.116109_b1","doi-asserted-by":"crossref","DOI":"10.1145\/3440207","article-title":"A survey on trajectory data management, analytics, and learning","volume":"54","author":"Wang","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.knosys.2026.116109_b2","series-title":"Forty-Second International Conference on Machine Learning","first-page":"n\/a","article-title":"GTR: A general, multi-view, and dynamic framework for trajectory representation learning","author":"Wang","year":"2025"},{"key":"10.1016\/j.knosys.2026.116109_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114141","article-title":"Spatio-temporal meta-learning for trajectory representation learning","volume":"327","author":"Xu","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116109_b4","series-title":"2018 IEEE 34th International Conference on Data Engineering","first-page":"617","article-title":"Deep representation learning for trajectory similarity computation","author":"Li","year":"2018"},{"key":"10.1016\/j.knosys.2026.116109_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112207","article-title":"STTraj2Vec: A spatio-temporal trajectory representation learning approach","volume":"300","author":"Zhu","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116109_b6","series-title":"Contrastive trajectory similarity learning with dual-feature attention","author":"Chang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116109_b7","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.knosys.2026.116109_b8","series-title":"2021 IEEE 37th International Conference on Data Engineering","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.knosys.2026.116109_b9","series-title":"2017 International Joint Conference on Neural Networks","first-page":"3880","article-title":"Trajectory clustering via deep representation learning","author":"Yao","year":"2017"},{"key":"10.1016\/j.knosys.2026.116109_b10","series-title":"Statistics for Spatio-Temporal Data","author":"Cressie","year":"2015"},{"key":"10.1016\/j.knosys.2026.116109_b11","series-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"key":"10.1016\/j.knosys.2026.116109_b12","first-page":"1","article-title":"Trajectory similarity measurement with spatial-temporal graph contrastive learning for traffic networks","author":"Wang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.116109_b13","series-title":"Trajectory representation learning on road networks and grids with spatio-temporal dynamics","author":"Schestakov","year":"2025"},{"key":"10.1016\/j.knosys.2026.116109_b14","series-title":"SPATE-GAN: Improved generative modeling of dynamic spatio-temporal patterns with an autoregressive embedding loss","author":"Klemmer","year":"2021"},{"key":"10.1016\/j.knosys.2026.116109_b15","series-title":"Momentum contrast for unsupervised visual representation learning","author":"He","year":"2020"},{"key":"10.1016\/j.knosys.2026.116109_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106199","article-title":"Sparse and low-rank regularized deep subspace clustering","volume":"204","author":"Zhu","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116109_b17","series-title":"Proceedings of the 13th Asian Conference on Machine Learning","first-page":"1145","article-title":"Deep structural contrastive subspace clustering","volume":"vol. 157","author":"Peng","year":"2021"},{"key":"10.1016\/j.knosys.2026.116109_b18","series-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","first-page":"3687","article-title":"Self-supervised embedding for subspace clustering","author":"Zhu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116109_b19","doi-asserted-by":"crossref","unstructured":"B. Peng, J. Lu, G. Zhang, Z. Fang, On the Provable Importance of Gradients for Autonomous Language-Assisted Image Clustering, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2025, pp. 19805\u201319815.","DOI":"10.1109\/ICCV51701.2025.01842"},{"key":"10.1016\/j.knosys.2026.116109_b20","series-title":"Geolife GPS trajectory dataset - User Guide","author":"Zheng","year":"2011"},{"key":"10.1016\/j.knosys.2026.116109_b21","series-title":"ECML pkdd 2015: Taxi trajectory prediction (i)","author":"Kaggle and University of Porto","year":"2015"},{"key":"10.1016\/j.knosys.2026.116109_b22","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.knosys.2026.116109_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103231","article-title":"STR: Spatio-temporal trajectory representation learning with dual-focus encoder for whole trajectory similarity computation","volume":"123","author":"Li","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.knosys.2026.116109_b24","series-title":"Deep learning for trajectory data management and mining: A survey and beyond","author":"Chen","year":"2024"},{"key":"10.1016\/j.knosys.2026.116109_b25","series-title":"Are transformers effective for time series forecasting?","author":"Zeng","year":"2022"},{"key":"10.1016\/j.knosys.2026.116109_b26","series-title":"Proceedings of the 42nd International Conference on Machine Learning","first-page":"7763","article-title":"A closer look at transformers for time series forecasting: Understanding why they work and where they struggle","volume":"vol. 267","author":"Chen","year":"2025"},{"issue":"4","key":"10.1016\/j.knosys.2026.116109_b27","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/j.1538-4632.2010.00800.x","article-title":"Space, scale, and scaling in entropy maximizing","volume":"42","author":"Batty","year":"2010","journal-title":"Geogr. Anal."},{"issue":"1","key":"10.1016\/j.knosys.2026.116109_b28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1538-4632.1974.tb01014.x","article-title":"Spatial entropy","volume":"6","author":"Batty","year":"1974","journal-title":"Geogr. Anal."},{"issue":"2","key":"10.1016\/j.knosys.2026.116109_b29","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1093\/oxfordjournals.aob.a083391","article-title":"A new method for determining the type of distribution of plant individuals","volume":"18","author":"Hopkins","year":"1954","journal-title":"Ann. Botany"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512600835X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512600835X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:31:44Z","timestamp":1781004704000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095070512600835X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":29,"alternative-id":["S095070512600835X"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116109","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"STC-Traj2vec: Spatiotemporal context-integrated trajectory representation learning for clustering","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116109","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116109"}}