{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T04:13:41Z","timestamp":1745986421164,"version":"3.40.4"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China","award":["(No. 2021YFC2801001)"],"award-info":[{"award-number":["(No. 2021YFC2801001)"]}]},{"name":"Open Topic Fund Project of the State Key Laboratory of Maritime Technology and Safety"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s40747-025-01877-x","type":"journal-article","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T06:56:40Z","timestamp":1744441000000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention"],"prefix":"10.1007","volume":"11","author":[{"given":"Jinxu","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7249-698X","authenticated-orcid":false,"given":"Jin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiliang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lai","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Zhongdai","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Junxiang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,12]]},"reference":[{"key":"1877_CR1","doi-asserted-by":"crossref","unstructured":"Jiang C, Xiang X, Xiang G (2024) A joint multi-model machine learning prediction approach based on confidence for ship stability. Complex Intell Syst 10(3):3873\u20133890","DOI":"10.1007\/s40747-024-01363-w"},{"issue":"5","key":"1877_CR2","doi-asserted-by":"publisher","first-page":"3881","DOI":"10.1007\/s40747-022-00683-z","volume":"8","author":"X Wang","year":"2022","unstructured":"Wang X, Liu J, Liu X, Liu Z, Khalaf OI, Ji J, Ouyang Q (2022) Ship feature recognition methods for deep learning in complex marine environments. Complex Intell Syst 8(5):3881\u20133897","journal-title":"Complex Intell Syst"},{"key":"1877_CR3","doi-asserted-by":"crossref","unstructured":"Dong X, Shi P, Liang T, Yang A (2024) CTAFFNet: CNN\u2013transformer adaptive feature fusion object detection algorithm for complex traffic scenarios. Transp Res Rec. SAGE Publications Sage CA, Los Angeles, CA, 03611981241258753","DOI":"10.1177\/03611981241258753"},{"key":"1877_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.108956","volume":"228","author":"D-W Gao","year":"2021","unstructured":"Gao D-W, Zhu Y-S, Zhang J-F, He Y-K, Yan K, Yan B-R (2021) A novel MP-LSTM method for ship trajectory prediction based on AIS data. Ocean Eng 228:108956","journal-title":"Ocean Eng"},{"issue":"5","key":"1877_CR5","doi-asserted-by":"publisher","first-page":"3080","DOI":"10.1109\/TNSE.2022.3140529","volume":"9","author":"RW Liu","year":"2022","unstructured":"Liu RW, Liang M, Nie J, Lim WYB, Zhang Y, Guizani M (2022) Deep learning-powered vessel trajectory prediction for improving smart traffic services in maritime internet of things. IEEE Trans Netw Sci Eng 9(5):3080\u20133094","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"1877_CR6","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"1877_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.115886","volume":"287","author":"X Zhang","year":"2023","unstructured":"Zhang X, Liu J, Gong P, Chen C, Han B, Wu Z (2023) Trajectory prediction of seagoing ships in dynamic traffic scenes via a gated spatio-temporal graph aggregation network. Ocean Eng 287:115886","journal-title":"Ocean Eng"},{"key":"1877_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2024.102814","volume":"84","author":"X Dong","year":"2024","unstructured":"Dong X, Shi P, Qi H, Yang A, Liang T (2024) TS-BEV: BEV object detection algorithm based on temporal-spatial feature fusion. Displays 84:102814","journal-title":"Displays"},{"key":"1877_CR9","doi-asserted-by":"crossref","unstructured":"Zhang X, Liu J, Chen K, Gong P, Liu Y, Wu Z (2024) Learning dynamic interactions and long-term patterns with spatio-temporal graphs for multi-vessel trajectory prediction. IEEE Trans Intell Veh","DOI":"10.1109\/TIV.2024.3401864"},{"key":"1877_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.116159","volume":"289","author":"J Zhao","year":"2023","unstructured":"Zhao J, Yan Z, Zhou Z, Chen X, Wu B, Wang S (2023) A ship trajectory prediction method based on GAT and LSTM. Ocean Eng 289:116159","journal-title":"Ocean Eng"},{"issue":"6","key":"1877_CR11","doi-asserted-by":"publisher","first-page":"609","DOI":"10.3390\/jmse9060609","volume":"9","author":"S Guo","year":"2021","unstructured":"Guo S, Mou J, Chen L, Chen P (2021) An anomaly detection method for AIS trajectory based on kinematic interpolation. J Mar Sci Eng 9(6):609","journal-title":"J Mar Sci Eng"},{"key":"1877_CR12","doi-asserted-by":"crossref","unstructured":"Jie X, Chaozhong W, Zhijun C, Xiaoxuan C (2017) A novel estimation algorithm for interpolating ship motion. In: 2017 4th international conference on transportation information and safety (ICTIS). IEEE, pp 557\u2013562","DOI":"10.1109\/ICTIS.2017.8047821"},{"key":"1877_CR13","doi-asserted-by":"publisher","first-page":"6507","DOI":"10.1109\/TIP.2020.2990346","volume":"29","author":"TX Pham","year":"2020","unstructured":"Pham TX, Siarry P, Oulhadj H (2020) Segmentation of MR brain images through hidden Markov random field and hybrid metaheuristic algorithm. IEEE Trans Image Process 29:6507\u20136522","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"1877_CR14","doi-asserted-by":"publisher","first-page":"8900","DOI":"10.1109\/TII.2022.3222366","volume":"19","author":"H Liu","year":"2022","unstructured":"Liu H, Wang J, Yin X, Ding J, Yang LT, Yao T, Yang J, Gao Y (2022) Tensor-train-based multiuser multivariate multiorder physical Markov process informed multimodal prediction for industrial trajectory applications. IEEE Trans Ind Inf 19(8):8900\u20138909","journal-title":"IEEE Trans Ind Inf"},{"key":"1877_CR15","doi-asserted-by":"crossref","unstructured":"Guo S, Liu C, Guo Z, Feng Y, Hong F, Huang H (2018) Trajectory prediction for ocean vessels base on k-order multivariate Markov chain. In: Wireless algorithms, systems, and applications: 13th international conference, WASA 2018, Tianjin, China, June 20\u201322, 2018, proceedings 13. Springer, pp 140\u2013150","DOI":"10.1007\/978-3-319-94268-1_12"},{"key":"1877_CR16","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.oceaneng.2018.04.026","volume":"160","author":"M Abdelaal","year":"2018","unstructured":"Abdelaal M, Fr\u00e4nzle M, Hahn A (2018) Nonlinear model predictive control for trajectory tracking and collision avoidance of underactuated vessels with disturbances. Ocean Eng 160:168\u2013180","journal-title":"Ocean Eng"},{"key":"1877_CR17","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.oceaneng.2017.04.017","volume":"138","author":"LP Perera","year":"2017","unstructured":"Perera LP (2017) Navigation vector based ship maneuvering prediction. Ocean Eng 138:151\u2013160","journal-title":"Ocean Eng"},{"issue":"4","key":"1877_CR18","doi-asserted-by":"publisher","first-page":"3696","DOI":"10.1109\/TITS.2020.3040268","volume":"23","author":"Z Xiao","year":"2020","unstructured":"Xiao Z, Fu X, Zhang L, Zhang W, Liu RW, Liu Z, Goh RSM (2020) Big data driven vessel trajectory and navigating state prediction with adaptive learning, motion modeling and particle filtering techniques. IEEE Trans Intell Transp Syst 23(4):3696\u20133709","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"1877_CR19","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1080\/19475683.2020.1840434","volume":"27","author":"D Alizadeh","year":"2021","unstructured":"Alizadeh D, Alesheikh AA, Sharif M (2021) Prediction of vessels locations and maritime traffic using similarity measurement of trajectory. Ann GIS 27(2):151\u2013162","journal-title":"Ann GIS"},{"key":"1877_CR20","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/j.oceaneng.2019.04.024","volume":"182","author":"H Rong","year":"2019","unstructured":"Rong H, Teixeira A, Soares CG (2019) Ship trajectory uncertainty prediction based on a gaussian process model. Ocean Eng 182:499\u2013511","journal-title":"Ocean Eng"},{"key":"1877_CR21","doi-asserted-by":"crossref","unstructured":"Dalsnes BR, Hexeberg S, Fl\u00e5ten AL, Eriksen B-OH, Brekke EF (2018) The neighbor course distribution method with gaussian mixture models for ais-based vessel trajectory prediction. In: 2018 21st international conference on information fusion (FUSION). IEEE, pp 580\u2013587","DOI":"10.23919\/ICIF.2018.8455607"},{"key":"1877_CR22","doi-asserted-by":"crossref","unstructured":"Cilliers H, Engelbrecht AP (2020) Fitting gaussian mixture models using cooperative particle swarm optimization. In: Swarm intelligence: 12th international conference, ANTS 2020, Barcelona, Spain, October 26\u201328, 2020, proceedings 12. Springer, pp 298\u2013305","DOI":"10.1007\/978-3-030-60376-2_24"},{"key":"1877_CR23","doi-asserted-by":"crossref","unstructured":"\u00dcney M, Millefiori LM, Braca P (2019) Data driven vessel trajectory forecasting using stochastic generative models. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 8459\u20138463","DOI":"10.1109\/ICASSP.2019.8683444"},{"issue":"1","key":"1877_CR24","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1017\/S0373463320000442","volume":"74","author":"D Alizadeh","year":"2021","unstructured":"Alizadeh D, Alesheikh AA, Sharif M (2021) Vessel trajectory prediction using historical automatic identification system data. J Navig 74(1):156\u2013174","journal-title":"J Navig"},{"issue":"2","key":"1877_CR25","first-page":"1773","volume":"24","author":"Y Xiao","year":"2022","unstructured":"Xiao Y, Li X, Yao W, Chen J, Hu Y (2022) Bidirectional data-driven trajectory prediction for intelligent maritime traffic. IEEE Trans Intell Transp Syst 24(2):1773\u20131785","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"8","key":"1877_CR26","doi-asserted-by":"publisher","first-page":"4073","DOI":"10.3390\/app12084073","volume":"12","author":"L Qian","year":"2022","unstructured":"Qian L, Zheng Y, Li L, Ma Y, Zhou C, Zhang D (2022) A new method of inland water ship trajectory prediction based on long short-term memory network optimized by genetic algorithm. Appl Sci 12(8):4073","journal-title":"Appl Sci"},{"key":"1877_CR27","doi-asserted-by":"publisher","first-page":"45600","DOI":"10.1109\/ACCESS.2021.3066463","volume":"9","author":"S Mehri","year":"2021","unstructured":"Mehri S, Alesheikh AA, Basiri A (2021) A contextual hybrid model for vessel movement prediction. IEEE Access 9:45600\u201345613","journal-title":"IEEE Access"},{"key":"1877_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.114248","volume":"277","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Han Z, Zhou X, Li B, Zhang L, Zhen E, Wang S, Zhao Z, Guo Z (2023) METO-S2S: a S2S based vessel trajectory prediction method with multiple-semantic encoder and type-oriented decoder. Ocean Eng 277:114248","journal-title":"Ocean Eng"},{"key":"1877_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111126","volume":"283","author":"X Li","year":"2024","unstructured":"Li X, Liu J, Xie Y, Gong P, Zhang X, He H (2024) MAGDRA: a multi-modal attention graph network with dynamic routing-by-agreement for multi-label emotion recognition. Knowl-Based Syst 283:111126","journal-title":"Knowl-Based Syst"},{"key":"1877_CR30","doi-asserted-by":"crossref","unstructured":"Chen J, Zhang J, Chen H, Zhao Y, Wang H (2023) A TDV attention-based BiGRU network for AIS-based vessel trajectory prediction. Iscience 26(4)","DOI":"10.1016\/j.isci.2023.106383"},{"key":"1877_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.117431","volume":"300","author":"H Xue","year":"2024","unstructured":"Xue H, Wang S, Xia M, Guo S (2024) G-Trans: a hierarchical approach to vessel trajectory prediction with GRU-based transformer. Ocean Eng 300:117431","journal-title":"Ocean Eng"},{"key":"1877_CR32","doi-asserted-by":"crossref","unstructured":"Cheng J, Wang J, Zhang Z, Yuan J, Shao W (2024) An improved AIS trajectory prediction model based on Transformer. In: Seventh international conference on traffic engineering and transportation system (ICTETS 2023), vol 13064. SPIE, pp 264\u2013270","DOI":"10.1117\/12.3015737"},{"key":"1877_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2023.129275","volume":"630","author":"Y Wang","year":"2023","unstructured":"Wang Y, Liu J, Liu RW, Wu W, Liu Y (2023) Interval prediction of vessel trajectory based on lower and upper bound estimation and attention-modified LSTM with Bayesian optimization. Physica A 630:129275","journal-title":"Physica A"},{"issue":"4","key":"1877_CR34","doi-asserted-by":"publisher","first-page":"880","DOI":"10.3390\/jmse11040880","volume":"11","author":"D Jiang","year":"2023","unstructured":"Jiang D, Shi G, Li N, Ma L, Li W, Shi J (2023) TRFM-LS: Transformer-based deep learning method for vessel trajectory prediction. J Mar Sci Eng 11(4):880","journal-title":"J Mar Sci Eng"},{"key":"1877_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2023.103152","volume":"175","author":"H Li","year":"2023","unstructured":"Li H, Jiao H, Yang Z (2023) AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods. Transp Res Part E Logist Transp Rev 175:103152","journal-title":"Transp Res Part E Logist Transp Rev"},{"key":"1877_CR36","unstructured":"Woo G, Liu C, Sahoo D, Kumar A, Hoi S (2022) Cost: contrastive learning of disentangled seasonal-trend representations for time series forecasting. arXiv preprint arXiv:2202.01575"},{"key":"1877_CR37","doi-asserted-by":"crossref","unstructured":"Cirstea R-G, Guo C, Yang B, Kieu T, Dong X, Pan S (2022) TriFormer: triangular, variable-specific attentions for long sequence multivariate time series forecasting\u2014full version. arXiv preprint arXiv:2204.13767","DOI":"10.24963\/ijcai.2022\/277"},{"issue":"1","key":"1877_CR38","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/s40537-022-00599-y","volume":"9","author":"AP Wibawa","year":"2022","unstructured":"Wibawa AP, Utama ABP, Elmunsyah H, Pujianto U, Dwiyanto FA, Hernandez L (2022) Time-series analysis with smoothed convolutional neural network. J Big Data 9(1):44","journal-title":"J Big Data"},{"issue":"7","key":"1877_CR39","doi-asserted-by":"publisher","first-page":"5374","DOI":"10.1109\/JIOT.2020.3028743","volume":"8","author":"RW Liu","year":"2020","unstructured":"Liu RW, Nie J, Garg S, Xiong Z, Zhang Y, Hossain MS (2020) Data-driven trajectory quality improvement for promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems. IEEE Internet Things J 8(7):5374\u20135385","journal-title":"IEEE Internet Things J"},{"key":"1877_CR40","doi-asserted-by":"crossref","unstructured":"Liu J, Shi G, Zhu K (2020) Online multiple outputs least-squares support vector regression model of ship trajectory prediction based on automatic information system data and selection mechanism. IEEE Access 8:154727\u2013154745","DOI":"10.1109\/ACCESS.2020.3018749"},{"key":"1877_CR41","doi-asserted-by":"crossref","unstructured":"Zhu F, Li J, Bi Q, Lai M (2023) Research on ship track prediction method based on improved PSO-BP algorithm. In: Sixth international conference on traffic engineering and transportation system (ICTETS 2022), vol 12591. SPIE, pp 775\u2013780","DOI":"10.1117\/12.2668566"},{"key":"1877_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2024.104231","volume":"153","author":"Z Liu","year":"2024","unstructured":"Liu Z, Qi W, Zhou S, Zhang W, Jiang C, Jie Y, Li C, Guo Y, Guo J (2024) Hybrid deep learning models for ship trajectory prediction in complex scenarios based on AIS data. Appl Ocean Res 153:104231","journal-title":"Appl Ocean Res"},{"key":"1877_CR43","doi-asserted-by":"crossref","unstructured":"Zhang Z, Ni G, Xu Y (2020) Ship trajectory prediction based on LSTM neural network. In: 2020 IEEE 5th information technology and mechatronics engineering conference (ITOEC). IEEE, pp 1356\u20131364","DOI":"10.1109\/ITOEC49072.2020.9141702"},{"key":"1877_CR44","unstructured":"Zhang Y, Yan J (2022) CrossFormer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The eleventh international conference on learning representations"},{"key":"1877_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.117572","volume":"301","author":"X Huang","year":"2024","unstructured":"Huang X, Tang J, Shen Y (2024) Long time series of ocean wave prediction based on PatchTST model. Ocean Eng 301:117572","journal-title":"Ocean Eng"},{"key":"1877_CR46","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01877-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01877-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01877-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T10:37:47Z","timestamp":1745923067000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01877-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,12]]},"references-count":46,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["1877"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01877-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,4,12]]},"assertion":[{"value":"1 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"239"}}