{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T10:45:34Z","timestamp":1769165134826,"version":"3.49.0"},"reference-count":31,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T00:00:00Z","timestamp":1519257600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472403"],"award-info":[{"award-number":["61472403"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61303243"],"award-info":[{"award-number":["61303243"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2018,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Trajectory clustering, which aims at discovering groups of similar trajectories, has long been considered as a corner stone task for revealing movement patterns as well as facilitating higher level applications such as location prediction and activity recognition. Although a plethora of trajectory clustering techniques have been proposed, they often rely on spatio\u2010temporal similarity measures that are not space and time invariant. As a result, they cannot detect trajectory clusters where the within\u2010cluster similarity occurs in different regions and time periods. In this paper, we revisit the trajectory clustering problem by learning quality low\u2010dimensional representations of the trajectories. We first use a sliding window to extract a set of moving behaviour features that capture space\u2010 and time\u2010invariant characteristics of the trajectories. With the feature extraction module, we transform each trajectory into a feature sequence to describe object movements and further employ a sequence\u2010to\u2010sequence auto\u2010encoder to learn fixed\u2010length deep representations. The learnt representations robustly encode the movement characteristics of the objects and thus lead to space\u2010 and time\u2010invariant clusters. We evaluate the proposed method on both synthetic and real data and observe significant performance improvements over existing methods.<\/jats:p>","DOI":"10.1111\/exsy.12252","type":"journal-article","created":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T05:22:43Z","timestamp":1519276963000},"source":"Crossref","is-referenced-by-count":44,"title":["Learning deep representation for trajectory clustering"],"prefix":"10.1111","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1778-8319","authenticated-orcid":false,"given":"Di","family":"Yao","sequence":"first","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences Beijing China"},{"name":"University of Chinese Academy of Sciences Beijing China"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Illinois at Urbana\u2010Champaign Urbana IL USA"}]},{"given":"Zhihua","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences Beijing China"},{"name":"University of Chinese Academy of Sciences Beijing China"}]},{"given":"Qin","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology Beijing Normal University Beijing China"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Technologies University of Sydney Sydney Australia"}]},{"given":"Jianhui","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences Beijing China"},{"name":"University of Chinese Academy of Sciences Beijing China"}]},{"given":"Jingping","family":"Bi","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences Beijing China"},{"name":"University of Chinese Academy of Sciences Beijing China"}]}],"member":"311","published-online":{"date-parts":[[2018,2,22]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"Alahi A. Vignesh\u00a0Ramanathan K. G. Robicquet A. Li F.\u2010F. &Savarese S.(2016).Social LSTM: Human trajectory prediction in crowded spaces InIEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas pp.961\u2013971.","DOI":"10.1109\/CVPR.2016.110"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2547641"},{"key":"e_1_2_8_6_1","doi-asserted-by":"crossref","unstructured":"Chen L. \u00d6zsu M. T. &Oria V.(2005).Robust and fast similarity search for moving object trajectories. InProceedings of the 2005 ACM SIGMOD International Conference on Management of Data (SIGMOD '05) ACM New York NY USA pp.491\u2013502.","DOI":"10.1145\/1066157.1066213"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","unstructured":"Chen Q. Song X. Yamada H. &Shibasaki R.(2016).Learning deep representation from big and heterogeneous data for traffic accident inference. InProceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI'16) AAAI Press Phoenix Arizona pp.338\u2013344.","DOI":"10.1609\/aaai.v30i1.10011"},{"key":"e_1_2_8_8_1","doi-asserted-by":"crossref","unstructured":"Cho K. van\u00a0Merrienboer B. Bahdanau D. &Bengio Y.(2014).On the properties of neural machine translation: Encoder\u2010decoder approaches. InEMNLP Association for Computational Linguistics Doha Qatar pp.103\u2013111.","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_2_8_9_1","doi-asserted-by":"crossref","unstructured":"Chung Y.\u2010A. Wu C.\u2010C. Shen C.\u2010H. &Lee H.\u2010Y.(2016).Unsupervised learning of audio segment representations using sequence\u2010to\u2010sequence recurrent neural networks. InProc. Interspeech San Francisco.","DOI":"10.21437\/Interspeech.2016-82"},{"key":"e_1_2_8_10_1","unstructured":"Dai A. M. &Le Q. V.(2015).Semi\u2010supervised sequence learning. InAdvances in Neural Information Processing Systems 28 (NIPS) Montr\u00e9al pp.3079\u20133087."},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0158248"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0373463307004298"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2326082"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-011-0262-6"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25446-8_8"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(98)00030-7"},{"key":"e_1_2_8_17_1","doi-asserted-by":"crossref","unstructured":"Lee J.\u2010G. Han J. &Whang K.\u2010Y.(2007).Trajectory clustering: A partition\u2010and\u2010group framework. InProceedings of the 2007 ACM SIGMOD International Conference on Management of Data (SIGMOD) ACM New York NY USA pp.593\u2013604.","DOI":"10.1145\/1247480.1247546"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12098-5_3"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2013.2272792"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.35"},{"key":"e_1_2_8_21_1","doi-asserted-by":"crossref","unstructured":"Mazzarella F. Vespe M. Damalas D. &Osio G.(2014).Discovering vessel activities at sea using AIS data: Mapping of fishing footprints. In17th International Conference on Information Fusion (FUSION) Salamanca pp.1\u20137.","DOI":"10.1109\/SDF.2015.7347707"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2016.2520371"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2004.04.003"},{"key":"e_1_2_8_24_1","unstructured":"Srivastava N. Mansimov E. &Salakhutdinov R.(2015).Unsupervised learning of video representations using lstms. In Francis Bach and David Blei (Eds.)Proceedings of the 32nd International Conference on International Conference on Machine Learning - (ICML'15) Vol. 37.JMLR.org pp.843\u2013852."},{"key":"e_1_2_8_25_1","unstructured":"Sutskever I. Vinyals O. &Le Q. V.(2014).Sequence to sequence learning with neural networks. InProceedings of the 27th International Conference on Neural Information Processing Systems - (NIPS'14) Vol. 2. MIT Press Cambridge MA USA pp.3104\u20133112."},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46257-8_25"},{"key":"e_1_2_8_27_1","doi-asserted-by":"crossref","unstructured":"Yao D. Zhang C. Zhu Z. Huang J. &Bi J.(2017).Trajectory clustering via deep representation learning. In2017 International Joint Conference on Neural Networks IJCNN 2016 Anchorage AK US May 13\u201019 2017 pp.4097\u20134104.","DOI":"10.1109\/IJCNN.2017.7966345"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-016-9477-7"},{"key":"e_1_2_8_29_1","doi-asserted-by":"crossref","unstructured":"Yuan Q. Zhang W. Zhang C. Geng X. Cong G. &Han J.(2017).PRED: Periodic region detection for mobility modeling of social media users. InWSDM ACM pp.263\u2013272.","DOI":"10.1145\/3018661.3018680"},{"issue":"9","key":"e_1_2_8_30_1","first-page":"769","article-title":"Splitter: Mining fine\u2010grained sequential patterns in semantic trajectories","volume":"7","author":"Zhang C.","year":"2014","journal-title":"PVLDB"},{"key":"e_1_2_8_31_1","doi-asserted-by":"crossref","unstructured":"Zhang C. Zhang K. Yuan Q. Zhang L. Hanratty T. &Han J.(2016).Gmove: Group\u2010level mobility modeling using geo\u2010tagged social media. InProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16).ACM New York NY USA 1305\u20131314.","DOI":"10.1145\/2939672.2939793"},{"issue":"3","key":"e_1_2_8_32_1","first-page":"29:1","article-title":"Trajectory data mining: An overview","volume":"6","author":"Zheng Y.","year":"2015","journal-title":"ACM TIST"}],"container-title":["Expert Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2Fexsy.12252","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/exsy.12252","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T23:17:40Z","timestamp":1751411860000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/exsy.12252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,22]]},"references-count":31,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,4]]}},"alternative-id":["10.1111\/exsy.12252"],"URL":"https:\/\/doi.org\/10.1111\/exsy.12252","archive":["Portico"],"relation":{},"ISSN":["0266-4720","1468-0394"],"issn-type":[{"value":"0266-4720","type":"print"},{"value":"1468-0394","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,22]]},"article-number":"e12252"}}