Maritime Transportation

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19 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

TempTPI: Informer-Based trajectory prediction for maritime vessels
Sep 9, 2026Kevin Ferneding, Veronika Lietavcova, Aleksandra M. Blachowiak +1Vessel Trajectory Prediction Maritime Transportation
Technical University of Denmark Anker Engelunds Vej 101 2800 Kongens Lyngby, Denmark
Time-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1,Hz Operational Data, CCGS Sir Wilfrid Laurier
Aug 17, 2026Samarasimha Reddy Chittamuru, Ayhan Akinturk, Allison Kennedy +2Maritime Transportation Temporal Data Leakage
National Research Council Canada, Ocean, Coastal and River Engineering, P.O. Box 12093, St. John’s, A1B 3T5, NL, Canada · Department of Computer Science, Memorial University of Newfoundland, 40 Arctic Avenue, St. John’s, A1B 3X5, NL, Canada
Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios
Aug 8, 2026Julius Wirbel, P. Nicholas Hansen, Line K. H. Clemmensen +1LLM Evaluation Maritime Navigation
Technical University of Denmark, Kongens Lyngby, Denmark · University of Copenhagen, Copenhagen, Denmark
Vessel Trajectory Prediction using COLREGs-aware Optimal Planning
Jul 17, 2026David Kaikkonen, Fredrik Ljungberg, Erik FriskTrajectory Prediction Trajectory Generation
ABB Corporate Research, Västerås, Sweden · Department of Electrical Engineering, Linköping University, Sweden
Sea-Scan: High-Accuracy, ML-based Dark Vessel Detection and Localisation via Weakly Supervised DAS Monitoring
Jun 19, 2026Tian Tian, Agastya Raj, Lara Flanagan +2Distributed Acoustic Sensing Object Detection
School of Computer Science and Statistics, IRIS Research Group, ADAPT Research Centre, Trinity College Dublin, Ireland · School of Engineering, ADAPT Research Centre, Trinity College Dublin, Ireland
EnvShip-Bench: An Environment-Enhanced Benchmark for Short-Term Vessel Trajectory Prediction
Jun 13, 2026Kun Ma, Qilong Han, Chengjing Song +3Forecasting Benchmarks Vessel Trajectory Prediction
Harbin Engineering University Harbin, China · Politecnico di Torino Turin, Italy · Institute of Software, Chinese Academy of Sciences Beijing, China
Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
Jun 7, 2026Hongwei Wang, Miao Zhou, Fengde Wang +10RL for Language Models Vessel Trajectory Prediction
Institute of High Performance Computing (IHPC), A*STAR, Singapore 138632, Singapore. · The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China. · Meituan Inc., Shenzhen, China. +3
Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Head
Jun 5, 2026Kyeongjun Lee, Heeyoung KimSpatiotemporal Forecasting Maritime Transportation
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
MariData: One-Step Unpaired Image Translation for Maritime Environments
Jun 2, 2026Santeri Henriksson, Mehdi Asadi, Amin Majd +1Image-to-Image Translation Synthetic Data Generation
AIS Lab, Turku University of Applied Sciences, Turku, Finland
Historical Knowledge Graphs for Global Maritime Estimated Time of Arrival
May 18, 2026Neofytos DimitriouKG Construction Maritime Transportation
Maritime Digitalization Centre Cyprus Marine and Maritime Institute Larnaca 6300, Cyprus
A Retrieval-Enhanced Transformer for Multi-Step Port-of-Call Sequence Prediction in Global Liner Shipping
May 15, 2026Yanzhao Su, Fang He, Yineng WangTransformer Vessel Trajectory Prediction
Department of Industrial Engineering, Tsinghua University, Beijing 100084, P.R. China · Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong
WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects
Apr 9, 2026Junxiong Liang, Mengwei Bao, Tianxiang Wang +3Ship Detection Maritime Transportation
School of Navigation, Wuhan University of Technology, Wuhan 430063, China · Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China · State Key Laboratory of Maritime Technology and Safety, Wuhan 430063, China +2
CARGO: Carbon-Aware Gossip Orchestration in Smart Shipping
Mar 29, 2026Alexandros S. Kalafatelis, Nikolaos Nomikos, Vasileios Nikolakakis +2Carbon-Aware Computing Federated Learning
Department of Ports Management and Shipping, National and Kapodistrian University of Athens, 34400 Euboea, Greece · Laskaridis Shipping Co. Ltd, 14562, Kifisia Greece
Physics-informed offline reinforcement learning eliminates catastrophic fuel waste in maritime routing
Mar 18, 2026Aniruddha Bora, Julie Chalfant, Chryssostomos ChryssostomidisMaritime Transportation Offline RL
Department of Computer Science, Texas State University, San Marcos, TX 78666, USA · MIT Sea Grant College Program, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Context-Enriched Natural Language Descriptions of Vessel Trajectories
Mar 8, 2026Kostas Patroumpas, Alexandros Troupiotis-Kapeliaris, Giannis Spiliopoulos +4Maritime Transportation Trajectory Representation Learning
Archimedes/Athena RC, Greece · University of the Aegean, Greece · Athena RC, Greece +1
Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters
Mar 4, 2026Xinyu Cui, Xuanfa Jin, Xue Yan +7Sim-to-Real Transfer Robot Navigation
Institute of Automation, CAS School of Artificial Intelligence, UCAS Beijing, China · Institute of Automation, CAS Beijing, China · Zhongguancun Academy Beijing, China +1
ShipTraj-R1: Reinforcing Ship Trajectory Prediction in Large Language Models via Group Relative Policy Optimization
Mar 3, 2026Yang Zhan, Yunhao Li, Zhang Chao +2Vessel Trajectory Prediction Trajectory Prediction
School of Artificial Intelligence, Optics, and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, China · Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong 999077, China · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Physics-Informed Machine Learning for Vessel Shaft Power and Fuel Consumption Prediction: Interpretable KAN-based Approach
Feb 25, 2026Hamza Haruna Mohammed, Dusica Marijan, Arnbjørn MaressaMaritime Transportation Physics-Informed ML
Simula Research Laboratory, Oslo, Norway · Navtor AS, Egersund, Norway
Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions
Feb 25, 2026Dusica Marijan, Hamza Haruna Mohammed, Bakht ZamanMaritime Transportation Data Fusion
Simula Research Laboratory, Kristian Augusts 23, Oslo, 0164, Norway

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TempTPI: Informer-Based trajectory prediction for maritime vessels

Kevin Ferneding, Veronika Lietavcova, Aleksandra M. Blachowiak, Peder Heiselberg

Technical University of Denmark Anker Engelunds Vej 101 2800 Kongens Lyngby, Denmark

Vessel Trajectory Prediction Maritime Transportation Time Series Transformers

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Accurate long-term trajectory prediction for maritime vessels is essential for safety and logistical efficiency. While deep learning models, particularly Transformers, have shown promise in processing Automatic Identification System (AIS) data, they often struggle with the quadratic computational complexity of self-attention and the loss of accuracy over extended forecasting horizons. This study proposes TempTPI, a novel prediction framework that integrates an Informer-based encoder with a multi-channel temporal encoding mechanism. The Informer architecture leverages a ProbSparse self-attention mechanism to reduce computational overhead and focus on the most significant dependencies, while the temporal encoder utilizes Fourier-like frequency expansions to capture cyclic patterns (hourly, daily, and seasonal) in vessel behavior. We evaluate our model against the state-of-the-art TPTrans architecture using AIS data from Danish waters. Experimental results demonstrate that TempTPI consistently outperforms existing methods across prediction windows of 1 to 5 hours. Notably, at a 5-hour horizon, the proposed model achieves a 55% improvement in Mean Squared Error (MSE), offering a robust solution for long-range maritime situational awareness.