{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T23:20:44Z","timestamp":1773271244361,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031329098","type":"print"},{"value":"9783031329104","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-32910-4_15","type":"book-chapter","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T05:38:34Z","timestamp":1683783514000},"page":"204-220","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["The Abnormal Detection Method of Ship Trajectory with Adaptive Transformer Model Based on Migration Learning"],"prefix":"10.1007","author":[{"given":"Kexin","family":"Li","sequence":"first","affiliation":[]},{"given":"Jian","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Ranchong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yujun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zongming","family":"Li","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Miu","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"15_CR1","unstructured":"Zhang: Construction of intelligent transportation platform based on 5G vehicle network. Commun. Inf. Technol. (S2), 28\u201331 (2022)"},{"key":"15_CR2","first-page":"47","volume":"16","author":"W Feng","year":"2022","unstructured":"Feng, W.: Exploration of 5G ultra-remote coverage technology and application scenarios. Commun. World 16, 47\u201349 (2022)","journal-title":"Commun. World"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Theodoropoulos, P., Spandonidis, C.C., Giannopoulos, F., Fassois, S.: A deep learning-based fault detection model for optimization of shipping operations and enhancement of maritime safety. Sensors 21(16), 5658 (2021)","DOI":"10.3390\/s21165658"},{"issue":"5","key":"15_CR4","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TITS.2017.2724551","volume":"19","author":"E Tu","year":"2017","unstructured":"Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., Huang, G.B.: Exploiting AIS data for intelligent maritime navigation: a comprehensive survey from data to methodology. IEEE Trans. Intell. Transp. Syst. 19(5), 1559\u20131582 (2017)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"18","key":"15_CR5","doi-asserted-by":"publisher","first-page":"7036","DOI":"10.3390\/s22187036","volume":"22","author":"W Lee","year":"2022","unstructured":"Lee, W., Cho, S.W.: AIS trajectories simplification algorithm considering topographic information. Sensors 22(18), 7036 (2022)","journal-title":"Sensors"},{"issue":"3","key":"15_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1\u201358 (2009)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"9","key":"15_CR7","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1109\/TKDE.2013.184","volume":"26","author":"M Gupta","year":"2013","unstructured":"Gupta, M., Gao, J., Aggarwal, C.C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250\u20132267 (2013)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"12","key":"15_CR8","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1109\/TSMC.2017.2718220","volume":"48","author":"H Liu","year":"2017","unstructured":"Liu, H., Li, X., Li, J., Zhang, S.: Efficient outlier detection for high-dimensional data. IEEE Trans. Syst. Man Cybern. Syst. 48(12), 2451\u20132461 (2017)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"15_CR9","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/978-981-13-6473-0_28","volume-title":"Computational Intelligence and Intelligent Systems","author":"Z Zhang","year":"2019","unstructured":"Zhang, Z., Zhu, M., Qiu, J., Liu, C., Zhang, D., Qi, J.: Outlier detection based on cluster outlier factor and mutual density. In: Peng, Hu., Deng, C., Wu, Z., Liu, Y. (eds.) ISICA 2018. CCIS, vol. 986, pp. 319\u2013329. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-6473-0_28"},{"issue":"4","key":"15_CR10","doi-asserted-by":"publisher","first-page":"291","DOI":"10.18178\/ijmlc.2021.11.4.1050","volume":"11","author":"H Liu","year":"2021","unstructured":"Liu, H., Qiao, Y., Zhao, G., Cheng, J., Meng, Z.: Agricultural machinery abnormal trajectory recognition. Int. J. Mach. Learn. Comput. 11(4), 291\u2013297 (2021)","journal-title":"Int. J. Mach. Learn. Comput."},{"issue":"20","key":"15_CR11","doi-asserted-by":"publisher","first-page":"23046","DOI":"10.1109\/JSEN.2021.3105680","volume":"21","author":"Z Qiao","year":"2021","unstructured":"Qiao, Z., Zhao, L., Gu, L., Jiang, X., Li, R., Ge, L.: Research on abnormal pedestrian trajectory detection of dynamic crowds in public scenarios. IEEE Sens. J. 21(20), 23046\u201323054 (2021)","journal-title":"IEEE Sens. J."},{"key":"15_CR12","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1016\/j.ins.2022.06.039","volume":"608","author":"X Du","year":"2022","unstructured":"Du, X., Yu, J., Chu, Z., Jin, L., Chen, J.: Graph autoencoder-based unsupervised outlier detection. Inf. Sci. 608, 532\u2013550 (2022)","journal-title":"Inf. Sci."},{"issue":"10","key":"15_CR13","doi-asserted-by":"publisher","first-page":"3520","DOI":"10.3390\/s21103520","volume":"21","author":"H Wu","year":"2021","unstructured":"Wu, H., Tang, X., Wang, Z., Wang, N.: Uncovering abnormal behavior patterns from mobility trajectories. Sensors 21(10), 3520 (2021)","journal-title":"Sensors"},{"key":"15_CR14","unstructured":"Meng, Tang, Wang: LSTM-AdaBoost vehicle trajectory prediction model considering lane change intention. Comput. Eng. Appl. 58(13), 280\u2013287 (2022)"},{"key":"15_CR15","unstructured":"Wan, Pan, Zhen, Ship: Ship trajectory prediction based on CNN-GRU. J. Guangzhou Inst. Navig. 30(02), 12\u201318 (2022)"},{"key":"15_CR16","unstructured":"Cheng: Research on Collision Avoidance and Trajectory Prediction Technology of Aircraft Based on Machine Learning. Nanjing University of Posts and Telecommunications (2022)"},{"key":"15_CR17","unstructured":"Wang, Yuan, Li, Xiao: Ship trajectory prediction and navigation intention recognition in intersection waters. Traffic Inf. Saf. 40(04), 101\u2013109 (2022)"},{"key":"15_CR18","doi-asserted-by":"publisher","first-page":"32742","DOI":"10.1109\/ACCESS.2022.3161661","volume":"10","author":"\u00c1DM Miguel","year":"2022","unstructured":"Miguel, \u00c1.D.M., Jos\u00e9, M.A., Fernando, G.: Vehicles trajectory prediction using recurrent VAE network. IEEE Access 10, 32742\u201332749 (2022)","journal-title":"IEEE Access"},{"issue":"8","key":"15_CR19","doi-asserted-by":"publisher","first-page":"4417","DOI":"10.1002\/int.22724","volume":"37","author":"Z Lv","year":"2022","unstructured":"Lv, Z., Huang, X., Cao, W.: An improved GAN with transformers for pedestrian trajectory prediction models. Int. J. Intell. Syst. 37(8), 4417\u20134436 (2022)","journal-title":"Int. J. Intell. Syst."},{"key":"15_CR20","unstructured":"Chen, Zhu, Yan: Ship track prediction based on LSTM. Mar. Eng. (06), 121\u2013125 (2019)"},{"issue":"12","key":"15_CR21","first-page":"2346","volume":"31","author":"J Lu","year":"2018","unstructured":"Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346\u20132363 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Bertsimas, D., Sim, M., Zhang: Adaptive distributionally robust optimization. Manag. Sci. 65(2), 604\u2013618 (2019)","DOI":"10.1287\/mnsc.2017.2952"},{"issue":"14","key":"15_CR23","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/bioinformatics\/btl242","volume":"22","author":"KM Borgwardt","year":"2006","unstructured":"Borgwardt, K.M., Gretton, A., Rasch, M.J.: Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14), 49\u201357 (2006)","journal-title":"Bioinformatics"}],"container-title":["Lecture Notes in Computer Science","Spatial Data and Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-32910-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T05:41:40Z","timestamp":1683783700000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-32910-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031329098","9783031329104"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-32910-4_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SpatialDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Spatial Data and Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanchang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"spatialdi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}