{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:49:42Z","timestamp":1766159382786,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031568251"},{"type":"electronic","value":"9783031568268"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-56826-8_12","type":"book-chapter","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:01:44Z","timestamp":1712034104000},"page":"160-170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards Vessel Arrival Time Prediction Through a\u00a0Deep Neural Network Cluster"],"prefix":"10.1007","author":[{"given":"Thimo F.","family":"Schindler","sequence":"first","affiliation":[]},{"given":"Jan-Hendrik","family":"Ohlendorf","sequence":"additional","affiliation":[]},{"given":"Klaus-Dieter","family":"Thoben","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.martra.2021.100038","volume":"2","author":"W Li","year":"2021","unstructured":"Li, W., Pundt, R., Miller-Hooks, E.: An updatable and comprehensive global cargo maritime network and strategic seaborne cargo routing model for global containerized and bulk vessel flow estimation. Maritime Transp. Res. 2, 1 (2021)","journal-title":"Maritime Transp. Res."},{"issue":"3","key":"12_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/jmse10030385","volume":"10","author":"J Mentjes","year":"2022","unstructured":"Mentjes, J., Wiards, H., Feuerstack, S.: Berthing assistant system using reference points. J. Marine Sci. Eng. 10(3), 1\u20132 (2022)","journal-title":"J. Marine Sci. Eng."},{"key":"12_CR3","unstructured":"Watson, R.T., Holm, H., Lind, M.: Green steaming: a methodology for estimating carbon emissions avoided. In: Thirty Sixth International Conference on Information Systems, pp. 1\u201315 (2015)"},{"key":"12_CR4","unstructured":"Faber, J., et al.: Greenhouse gas study (2020)"},{"key":"12_CR5","series-title":"Lecture Notes in Logistics","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/978-3-030-44783-0_7","volume-title":"Dynamics in Logistics","author":"J Franzkeit","year":"2020","unstructured":"Franzkeit, J., Pache, H., Jahn, C.: Investigation of vessel waiting times using AIS data. In: Freitag, M., Haasis, H.-D., Kotzab, H., Pannek, J. (eds.) LDIC 2020. LNL, pp. 70\u201378. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-44783-0_7"},{"key":"12_CR6","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.trd.2017.05.002","volume":"54","author":"G Venturini","year":"2017","unstructured":"Venturini, G., Iris, \u00c7., Kontovas, C.A., Larsen, A.: The multi-port berth allocation problem with speed optimization and emission considerations. Transp. Res. Part D: Transp. Environ. 54, 142\u2013159 (2017)","journal-title":"Transp. Res. Part D: Transp. Environ."},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Noman, A., Heuermann, A., Wiesner, S., Thoben, K.-D.: Towards data-driven GRU based ETA prediction approach for vessels on both inland natural and artificial waterways, pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/ITSC48978.2021.9564883"},{"issue":"7","key":"12_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app10072325","volume":"10","author":"M Abebe","year":"2020","unstructured":"Abebe, M., Shin, Y., Noh, Y., Lee, S., Lee, I.: Machine learning approaches for ship speed prediction towards energy efficient shipping. Appl. Sci. 10(7), 1\u201317 (2020)","journal-title":"Appl. Sci."},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Abualhaol, I., Falcon, R., Abielmona, R., Petriu, E.: Data-driven vessel service time forecasting using long short-term memory recurrent neural networks. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE (2018)","DOI":"10.1109\/BigData.2018.8622626"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Hexeberg, S., Fl\u00e5ten, A.L., Eriksen, B.-O.H., Brekke, E.F.: AIS-based vessel trajectory prediction. In: 2017 20th International Conference on Information Fusion (Fusion), pp. 1\u20138 (2017)","DOI":"10.23919\/ICIF.2017.8009762"},{"key":"12_CR11","unstructured":"De\u00a0Br\u00e9bisson, A., Simon, T., Auvolat, A., Vincent, P., Bengio, Y.: Artificial neural networks applied to taxi destination prediction (2015)"},{"issue":"6","key":"12_CR12","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1080\/01441647.2019.1649315","volume":"39","author":"D Yang","year":"2019","unstructured":"Yang, D., Wu, L., Wang, S., Jia, H., Li, K.X.: How big data enriches maritime research - a critical review of automatic identification system (AIS) data applications. Transp. Rev. 39(6), 755\u2013773 (2019)","journal-title":"Transp. Rev."},{"issue":"3","key":"12_CR13","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1017\/S0373463307004298","volume":"60","author":"A Harati-Mokhtari","year":"2007","unstructured":"Harati-Mokhtari, A., Wall, A., Brooks, P., Wang, J.: Automatic identification system (AIS): data reliability and human error implications. J. Navig. 60(3), 373\u2013389 (2007)","journal-title":"J. Navig."},{"issue":"3","key":"12_CR14","first-page":"258","volume":"18","author":"W He","year":"2014","unstructured":"He, W.: Deep neural network based load forecast. Comput. Modell. New Technol. 18(3), 258\u2013262 (2014)","journal-title":"Comput. Modell. New Technol."},{"key":"12_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2015)"},{"issue":"56","key":"12_CR16","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"12_CR17","unstructured":"Pereyra, G., Tucker, G., Chorowski, J., Kaiser, U., Hinton, G.: Regularizing neural networks by penalizing confident output distributions (2017)"},{"key":"12_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/3-540-49430-8_3","volume-title":"Neural Networks: Tricks of the Trade","author":"L Prechelt","year":"1998","unstructured":"Prechelt, L.: Early stopping - but when? In: Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 1524, pp. 55\u201369. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/3-540-49430-8_3"}],"container-title":["Lecture Notes in Logistics","Dynamics in Logistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56826-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:04:23Z","timestamp":1712034263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56826-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031568251","9783031568268"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56826-8_12","relation":{},"ISSN":["2194-8917","2194-8925"],"issn-type":[{"type":"print","value":"2194-8917"},{"type":"electronic","value":"2194-8925"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LDIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Dynamics in Logistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bremen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 February 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 February 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ldic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.uni-bremen.de\/ldic-conference\/about-ldic","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}