{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T04:58:59Z","timestamp":1768971539303,"version":"3.49.0"},"reference-count":59,"publisher":"Informa UK Limited","issue":"1","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council of Taiwan","doi-asserted-by":"publisher","award":["112-2121-M-002-008"],"award-info":[{"award-number":["112-2121-M-002-008"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council of Taiwan","doi-asserted-by":"publisher","award":["113-2121-M-002-007"],"award-info":[{"award-number":["113-2121-M-002-007"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["JCYJ20200109141235597"],"award-info":[{"award-number":["JCYJ20200109141235597"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61761136008"],"award-info":[{"award-number":["61761136008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Connection Science"],"published-print":{"date-parts":[[2025,12,31]]},"DOI":"10.1080\/09540091.2025.2458502","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T19:20:41Z","timestamp":1738264841000},"update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":3,"title":["Generative adversarial deep learning model for producing location-based synthetic trajectory data"],"prefix":"10.1080","volume":"37","author":[{"given":"Alessandro","family":"Crivellari","sequence":"first","affiliation":[{"name":"National Taiwan University","place":["Taipei, Taiwan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhui","family":"Shi","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology","place":["Shenzhen, People\u2019s Republic of China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"301","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19990315)18:5<497::AID-SIM45>3.0.CO;2-#"},{"key":"e_1_3_4_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/0361198119855999"},{"key":"e_1_3_4_4_1","doi-asserted-by":"crossref","unstructured":"Baumann P. Kleiminger W. & Santini S. (2013 September). The influence of temporal and spatial features on the performance of next-place prediction algorithms. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing (pp. 449\u2013458).","DOI":"10.1145\/2493432.2493467"},{"key":"e_1_3_4_5_1","unstructured":"Behadili S. F. Bertelle C. & George L. E. (2018). Human trajectories characteristics. arXiv preprint arXiv:1807.06100."},{"key":"e_1_3_4_6_1","doi-asserted-by":"crossref","unstructured":"Bhargava P. Phan T. Zhou J. & Lee J. (2015 May). Who what when and where: Multi-dimensional collaborative recommendations using tensor factorization on sparse user-generated data. In Proceedings of the 24th international conference on World Wide Web (pp. 130\u2013140).","DOI":"10.1145\/2736277.2741077"},{"key":"e_1_3_4_7_1","doi-asserted-by":"publisher","DOI":"10.3758\/BF03193020"},{"key":"e_1_3_4_8_1","doi-asserted-by":"crossref","unstructured":"Cao C. & Li M. (2021 August). Generating mobility trajectories with retained data utility. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 2610\u20132620).","DOI":"10.1145\/3447548.3467158"},{"key":"e_1_3_4_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.03.120"},{"key":"e_1_3_4_10_1","unstructured":"Cheng C. Yang H. Lyu M. R. & King I. (2013 June). Where you like to go next: Successive point-of-interest recommendation. In Twenty-third international joint conference on Artificial Intelligence."},{"key":"e_1_3_4_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.02.079"},{"key":"e_1_3_4_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2031331.2031335"},{"key":"e_1_3_4_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2024.2312199"},{"key":"e_1_3_4_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8030134"},{"key":"e_1_3_4_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20123503"},{"key":"e_1_3_4_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep01376"},{"key":"e_1_3_4_17_1","doi-asserted-by":"crossref","unstructured":"De Montjoye Y. A. Quoidbach J. Robic F. & Pentland A. (2013). Predicting personality using novel mobile phone-based metrics. In Social computing behavioral-cultural modeling and prediction: 6th international conference SBP 2013 Washington DC USA April 2\u20135 2013. Proceedings 6 (pp. 48\u201355). Springer Berlin Heidelberg.","DOI":"10.1007\/978-3-642-37210-0_6"},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-8983-6_32"},{"key":"e_1_3_4_19_1","doi-asserted-by":"crossref","unstructured":"Feng J. Yang Z. Xu F. Yu H. Wang M. & Li Y. (2020 August). Learning to simulate human mobility. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 3426\u20133433).","DOI":"10.1145\/3394486.3412862"},{"key":"e_1_3_4_20_1","doi-asserted-by":"crossref","unstructured":"Gambs S. Killijian M. O. & del Prado Cortez M. N. (2012 April). Next place prediction using mobility Markov chains. In Proceedings of the first workshop on measurement privacy and mobility (pp. 1\u20136).","DOI":"10.1145\/2181196.2181199"},{"key":"e_1_3_4_21_1","first-page":"105","article-title":"Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users","volume":"19","author":"Gao S.","year":"2019","unstructured":"Gao, S., Rao, J., Liu, X., Kang, Y., Huang, Q., & App, J. (2019). Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users. Journal of Spatial Information Science, 19, 105\u2013129.","journal-title":"Journal of Spatial Information Science"},{"key":"e_1_3_4_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature06958"},{"key":"e_1_3_4_23_1","doi-asserted-by":"crossref","unstructured":"Gruteser M. & Grunwald D. (2003 May). Anonymous usage of location-based services through spatial and temporal cloaking. In Proceedings of the 1st international conference on Mobile systems applications and services (pp. 31\u201342).","DOI":"10.1145\/1066116.1189037"},{"key":"e_1_3_4_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwq248"},{"key":"e_1_3_4_25_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0170907"},{"key":"e_1_3_4_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10955-014-1024-9"},{"key":"e_1_3_4_27_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_4_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2014.02.011"},{"key":"e_1_3_4_29_1","doi-asserted-by":"publisher","DOI":"10.1080\/00045608.2015.1081120"},{"key":"e_1_3_4_30_1","doi-asserted-by":"crossref","unstructured":"Isaacman S. Becker R. C\u00e1ceres R. Martonosi M. Rowland J. Varshavsky A. & Willinger W. (2012 June). Human mobility modeling at metropolitan scales. In Proceedings of the 10th international conference on mobile systems applications and services (pp. 239\u2013252).","DOI":"10.1145\/2307636.2307659"},{"key":"e_1_3_4_31_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1524261113"},{"key":"e_1_3_4_32_1","doi-asserted-by":"crossref","unstructured":"Jiang W. Zhao W. X. Wang J. & Jiang J. (2023 June). Continuous trajectory generation based on two-stage GAN. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 4374\u20134382).","DOI":"10.1609\/aaai.v37i4.25557"},{"key":"e_1_3_4_33_1","doi-asserted-by":"crossref","unstructured":"Karatzoglou A. Jablonski A. & Beigl M. (2018 November). A Seq2Seq learning approach for modeling semantic trajectories and predicting the next location. In Proceedings of the 26th acm sigspatial international conference on advances in geographic information systems (pp. 528\u2013531).","DOI":"10.1145\/3274895.3274983"},{"key":"e_1_3_4_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12751"},{"key":"e_1_3_4_35_1","unstructured":"Kulkarni V. Tagasovska N. Vatter T. & Garbinato B. (2018). Generative models for simulating mobility trajectories. arXiv preprint arXiv:1811.12801."},{"key":"e_1_3_4_36_1","doi-asserted-by":"publisher","DOI":"10.3138\/X204-4223-57MK-8273"},{"key":"e_1_3_4_37_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16224522"},{"key":"e_1_3_4_38_1","doi-asserted-by":"publisher","DOI":"10.1080\/00045608.2015.1018773"},{"key":"e_1_3_4_39_1","doi-asserted-by":"crossref","unstructured":"McKenzie G. Janowicz K. & Seidl D. (2016). Geo-privacy beyond coordinates. In Geospatial data in a changing world: Selected papers of the 19th AGILE Conference on Geographic Information Science (pp. 157\u2013175). Springer International Publishing.","DOI":"10.1007\/978-3-319-33783-8_10"},{"key":"e_1_3_4_40_1","unstructured":"Mikolov T. Chen K. Corrado G. & Dean J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781."},{"key":"e_1_3_4_41_1","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","volume":"26","author":"Mikolov T.","year":"2013","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26, 3111\u20133119.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_4_42_1","doi-asserted-by":"crossref","unstructured":"Nergiz M. E. Atzori M. & Saygin Y. (2008 November). Towards trajectory anonymization: a generalization-based approach. In Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS (pp. 52\u201361).","DOI":"10.1145\/1503402.1503413"},{"key":"e_1_3_4_43_1","doi-asserted-by":"crossref","unstructured":"Niu B. Li Q. Zhu X. Cao G. & Li H. (2014 April). Achieving k-anonymity in privacy-aware location-based services. In IEEE INFOCOM 2014-IEEE conference on computer communications (pp. 754\u2013762). IEEE.","DOI":"10.1109\/INFOCOM.2014.6848002"},{"key":"e_1_3_4_44_1","first-page":"3812","article-title":"A non-parametric generative model for human trajectories","volume":"18","author":"Ouyang K.","year":"2018","unstructured":"Ouyang, K., Shokri, R., Rosenblum, D. S., & Yang, W. (2018, July). A non-parametric generative model for human trajectories. In IJCAI, 18, 3812\u20133817.","journal-title":"In IJCAI"},{"key":"e_1_3_4_45_1","doi-asserted-by":"crossref","unstructured":"Palanisamy B. & Liu L. (2011 April). Mobimix: Protecting location privacy with mix-zones over road networks. In 2011 IEEE 27th International conference on data engineering (pp. 494\u2013505). IEEE.","DOI":"10.1109\/ICDE.2011.5767898"},{"key":"e_1_3_4_46_1","doi-asserted-by":"crossref","unstructured":"Pennington J. Socher R. & Manning C. D. (2014 October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532\u20131543).","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_4_47_1","unstructured":"Rao J. Gao S. Kang Y. & Huang Q. (2020). LSTM-TrajGAN: A deep learning approach to trajectory privacy protection. arXiv preprint arXiv:2006.10521."},{"key":"e_1_3_4_48_1","doi-asserted-by":"publisher","DOI":"10.1111\/gean.12144"},{"key":"e_1_3_4_49_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2015.1101767"},{"key":"e_1_3_4_50_1","doi-asserted-by":"publisher","DOI":"10.3390\/s150715974"},{"key":"e_1_3_4_51_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-26752-4"},{"key":"e_1_3_4_52_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1177170"},{"key":"e_1_3_4_53_1","doi-asserted-by":"crossref","unstructured":"Song X. Zhang Q. Sekimoto Y. & Shibasaki R. (2014 August). Prediction of human emergency behavior and their mobility following large-scale disaster. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 5\u201314).","DOI":"10.1145\/2623330.2623628"},{"key":"e_1_3_4_54_1","doi-asserted-by":"crossref","unstructured":"Sunds\u00f8y P. Bjelland J. Reme B. A. Iqbal A. M. & Jahani E. (2016 January). Deep learning applied to mobile phone data for individual income classification. In 2016 International Conference on Artificial Intelligence: Technologies and Applications (pp. 96\u201399). Atlantis Press.","DOI":"10.2991\/icaita-16.2016.24"},{"key":"e_1_3_4_55_1","doi-asserted-by":"crossref","unstructured":"Xu F. Tu Z. Li Y. Zhang P. Fu X. & Jin D. (2017 April). Trajectory recovery from ash: User privacy is not preserved in aggregated mobility data. In Proceedings of the 26th international conference on World Wide Web (pp. 1241\u20131250).","DOI":"10.1145\/3038912.3052620"},{"key":"e_1_3_4_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2695438"},{"key":"e_1_3_4_57_1","doi-asserted-by":"crossref","unstructured":"Yu L. Zhang W. Wang J. & Yu Y. (2017 February). Seqgan: Sequence generative adversarial nets with policy gradient. In Proceedings of the AAAI conference on artificial intelligence (Vol. 31 No. 1).","DOI":"10.1609\/aaai.v31i1.10804"},{"issue":"1","key":"e_1_3_4_58_1","first-page":"567049","article-title":"Ensuring confidentiality of geocoded health data: Assessing geographic masking strategies for individual-level data","volume":"2014","author":"Zandbergen P. A.","year":"2014","unstructured":"Zandbergen, P. A. (2014). Ensuring confidentiality of geocoded health data: Assessing geographic masking strategies for individual-level data. Advances in Medicine, 2014(1), 567049.","journal-title":"Advances in Medicine"},{"key":"e_1_3_4_59_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.01.022"},{"key":"e_1_3_4_60_1","doi-asserted-by":"crossref","unstructured":"Zhu H. Yang X. Wang B. Wang L. & Lee W. C. (2019). Private trajectory data publication for trajectory classification. In Web Information Systems and Applications: 16th International Conference WISA 2019 Qingdao People\u2019s Republic of China September 20-22 2019 Proceedings 16 (pp. 347\u2013360). Springer International Publishing.","DOI":"10.1007\/978-3-030-30952-7_35"}],"container-title":["Connection Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/09540091.2025.2458502","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T03:38:08Z","timestamp":1766115488000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/09540091.2025.2458502"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,30]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,12,31]]}},"alternative-id":["10.1080\/09540091.2025.2458502"],"URL":"https:\/\/doi.org\/10.1080\/09540091.2025.2458502","relation":{},"ISSN":["0954-0091","1360-0494"],"issn-type":[{"value":"0954-0091","type":"print"},{"value":"1360-0494","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,30]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=ccos20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=ccos20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2024-08-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"2458502"}}