bgafpfИзвестия Балтийской государственной академии рыбопромыслового флота. Психолого-педагогические наукиTHE TIDINGS of the Baltic State Fishing Fleet Academy Psychological and pedagogical sciences (Theory and methods of professional education)2071-5331Калининградский государственный технический университет10.46845/2071-5331-2026-3-77-157-161bgafpf-604Research ArticleОБРАЗОВАТЕЛЬНЫЕ ТЕХНОЛОГИИТехнологии глубокого обучения для прогнозирования маршрутов движения судовDeep learning technologies for predicting ship routeshttps://orcid.org/0000-0002-1092-1474КовалишинП. Ю.KovalishinP. Yu.

Павел Юрьевич Ковалишин - кандидат филологических наук, доцент 

Калининград

 Candidate of Philology, Associate Professor 

pavelkovalishinkaliningrad@mail.ruhttps://orcid.org/0000-0002-0179-2332БураковскаяМ. В.BurakovskayaM. V.

Марина Васильевна Бураковская - кандидат технических наук, доцент 

Калининград

 Candidate of Technical Sciences, Associate Professor 

bgarf1988@inbox.ruБалтийская государственная академия рыбопромыслового флота ФГБОУ ВО "КГТУ"Russian Federation20263009202603(77)157161Copyright © Ковалишин П.Ю., Бураковская М.В., 20262026Ковалишин П.Ю., Бураковская М.В.Kovalishin P.Y., Burakovskaya M.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://brarf.journal.klgtu.ru/jour/article/view/604

Рассматриваются механизмы использования глубокого обучения для прогнозирования маршрутов движения судов. Интенсивность морского трафика стремительно растет: эффективное прогнозирование является одной из важнейших задач интеллектуальной транспортной системы. Глубокое обучение (Deep Learning, DL) – это подраздел машинного обучения, в котором используются многослойные искусственные нейронные сети для автоматического выявления сложных закономерностей в больших данных. В отличие от классического машинного обучения, DL-модели самостоятельно извлекают признаки (feature learning) без участия человека, что позволяет достигать высокой точности в компьютерном зрении, распознавании речи и генеративном ИИ. Глубокое обучение позволяет вычислительным моделям, состоящим из нескольких обрабатывающих слоев, изучать представления данных с несколькими уровнями абстракции.

This article examines the mechanisms for using deep learning to predict ship routes. As maritime traffic volumes rapidly increase, effective forecasting is a critical task for intelligent transportation systems. Deep learning (DL) is a subset of machine learning that uses multilayer artificial neural networks to automatically detect complex patterns in big data. Unlike classical machine learning, DL models independently extract features (feature learning) without human intervention, enabling high accuracy in computer vision, speech recognition, and generative AI. Deep learning enables computational models consisting of multiple processing layers to learn data representations with multiple levels of abstraction.

прогнозирование траффикаглубокое обучениенейронные сетивычислительные моделиtraffic forecastingdeep learningneural networkscomputational modelsReferencesLi, M. W., Han, D.F., Wang, W.L. Vessel traffic flow forecasting by RSVR with chaotic cloud simulated annealing genetic algorithm and KPCA // Neurocomputing. – 2015. – P. 243–255.Li, M. W., Han, D.F., Wang, W.L. Vessel traffic flow forecasting by RSVR with chaotic cloud simulated annealing genetic algorithm and KPCA // Neurocomputing. – 2015. – P. 243–255.He, Y. et al. An improved Kalman filter model for short-term vessel traffic flow prediction // Journal of Navigation. – 2017.He, Y. et al. An improved Kalman filter model for short-term vessel traffic flow prediction // Journal of Navigation. – 2017.Wang, Ch., Zhang, X., Chen, X., Li, R., Li, G. Vessel traffic flow forecasting based on BP neural network and residual analysis // 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS). – 2017. – Pр. 350–354.Wang, Ch., Zhang, X., Chen, X., Li, R., Li, G. Vessel traffic flow forecasting based on BP neural network and residual analysis // 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS). – 2017. – Pр. 350–354.Yang, L., Hao, Y., Liu, Q., Zhu X. Ship traffic volume forecast in bridge area based on enhanced hybrid radial basis function neural networks // Proceedings of the International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, 25–28. – June 2015. – Pр. 38–43.Yang, L., Hao, Y., Liu, Q., Zhu X. Ship traffic volume forecast in bridge area based on enhanced hybrid radial basis function neural networks // Proceedings of the International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, 25–28. – June 2015. – Pр. 38–43.Chen, L. et al. Stacked LSTM (long short-term memory) network for traffic congestion forecasting on realistic navigation maps // IEEE Transactions on Intelligent Transportation Systems. – 2016.Chen, L. et al. Stacked LSTM (long short-term memory) network for traffic congestion forecasting on realistic navigation maps // IEEE Transactions on Intelligent Transportation Systems. – 2016.Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F. Y. Traffic flow prediction with big data: A deep learning approach // IEEE Transactions on Intelligent Transportation Systems. – 2015. – 16(2). – Pр. 865–873.Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F. Y. Traffic flow prediction with big data: A deep learning approach // IEEE Transactions on Intelligent Transportation Systems. – 2015. – 16(2). – Pр. 865–873.Zhang, J., Zheng, Y., Qi D. Deep spatio-temporal residual networks for citywide crowd flow prediction // Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-17). – 2017.Zhang, J., Zheng, Y., Qi D. Deep spatio-temporal residual networks for citywide crowd flow prediction // Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-17). – 2017.Zhao, L., & Liu, Y. (2023). Hybrid Deep Learning Models for Real-time Ship Collision Avoidance and Path Planning. Reliability Engineering & System Safety, 231. – Рр. 108–124.Zhao, L., & Liu, Y. (2023). Hybrid Deep Learning Models for Real-time Ship Collision Avoidance and Path Planning. Reliability Engineering & System Safety, 231. – Рр. 108–124.Li, S. et al. A Transformer-based Framework for Vessel Trajectory Prediction with Attention Mechanisms // Ocean Engineering. – 2022. – 244. – Pр. 110–135.Li, S. et al. A Transformer-based Framework for Vessel Trajectory Prediction with Attention Mechanisms // Ocean Engineering. – 2022. – 244. – Pр. 110–135.Wang, X. et al. Spatiotemporal Graph Convolutional Networks for Vessel Traffic Flow Forecasting in Complex Waterways // IEEE Transactions on Intelligent Transportation Systems, 2023. – № 24(8). – Pр. 8567–8582.Wang, X. et al. Spatiotemporal Graph Convolutional Networks for Vessel Traffic Flow Forecasting in Complex Waterways // IEEE Transactions on Intelligent Transportation Systems, 2023. – № 24(8). – Pр. 8567–8582.Zhang, M. et al. Diffusion Models for AIS Data Recovery and Trajectory Generation in Marine Navigation // Journal of Marine Science and Engineering. 2024. – № 12(2). – P. 341.Zhang, M. et al. Diffusion Models for AIS Data Recovery and Trajectory Generation in Marine Navigation // Journal of Marine Science and Engineering. 2024. – № 12(2). – P. 341.

The authors declare that there are no conflicts of interest present.