# Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2101.02486 · HTML: https://mlsanity.com/paper/2101.02486/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Is Attention Interpretable?](https://mlsanity.com/paper/1906.03731/) (2019)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [On orthogonality and learning recurrent networks with long term dependencies](https://mlsanity.com/paper/1702.00071/) (2017)
- [Recurrent Orthogonal Networks and Long-Memory Tasks](https://mlsanity.com/paper/1602.06662/) (2016)
- [Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review](https://mlsanity.com/paper/1912.11676/) (2019)
- [A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams](https://mlsanity.com/paper/1806.03972/) (2018)
- [Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction](https://mlsanity.com/paper/1609.00203/) (2016)
- [Classification of cancer pathology reports: a large-scale comparative study](https://mlsanity.com/paper/2006.16370/) (2020)

## Built on by (most cited first, 30 of 32)

- [TrAISformer -- A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction](https://mlsanity.com/paper/2109.03958/) (2021)
- [NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanisms](https://mlsanity.com/paper/2106.11175/) (2021)
- [TrackGPT -- A generative pre-trained transformer for cross-domain entity trajectory forecasting](https://mlsanity.com/paper/2402.00066/) (2024)
- [Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting](https://mlsanity.com/paper/2310.18948/) (2023)
- [Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis](https://mlsanity.com/paper/2207.10939/) (2022)
- [Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning](https://mlsanity.com/paper/2202.13867/) (2022)
- [Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction with Uncertainty Estimation](https://mlsanity.com/paper/2205.05404/) (2022)
- [Improved context-sensitive transformer model for inland vessel trajectory prediction](https://mlsanity.com/paper/2406.02771/) (2024)
- [A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation](https://mlsanity.com/paper/2407.11084/) (2024)
- [Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles](https://mlsanity.com/paper/2409.17277/) (2024)
- [A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction](https://mlsanity.com/paper/2503.21834/) (2025)
- [Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Trajectories](https://mlsanity.com/paper/2107.01557/) (2021)
- [MSTFormer: Motion Inspired Spatial-temporal Transformer with Dynamic-aware Attention for long-term Vessel Trajectory Prediction](https://mlsanity.com/paper/2303.11540/) (2023)
- [A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data](https://mlsanity.com/paper/2303.14068/) (2023)
- [MLOps with Microservices: A Case Study on the Maritime Domain](https://mlsanity.com/paper/2506.06202/) (2025)
- [On Vessel Location Forecasting and the Effect of Federated Learning](https://mlsanity.com/paper/2405.19870/) (2024)
- [Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder Models](https://mlsanity.com/paper/2406.02770/) (2024)
- [DiffuTraj: A Stochastic Vessel Trajectory Prediction Approach via Guided Diffusion Process](https://mlsanity.com/paper/2410.09550/) (2024)
- [Predicting Barge Presence and Quantity on Inland Waterways using Vessel Tracking Data: A Machine Learning Approach](https://mlsanity.com/paper/2501.00615/) (2024)
- [STGDPM:Vessel Trajectory Prediction with Spatio-Temporal Graph Diffusion Probabilistic Model](https://mlsanity.com/paper/2503.08065/) (2025)
- [Unified Multimodal Vessel Trajectory Prediction with Explainable Navigation Intention](https://mlsanity.com/paper/2511.14265/) (2025)
- [WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory](https://mlsanity.com/paper/2512.13190/) (2025)
- [CmIVTP: Cross-modal Interaction-based Vessel Trajectory Prediction for Maritime Intelligence](https://mlsanity.com/paper/2605.26524/) (2026)
- [EnvShip: A Unified Framework for Context-Aware and Cross-Region Vessel Trajectory Forecasting](https://mlsanity.com/paper/2606.15240/) (2026)
- [Large Deviations for Classification Performance Analysis of Machine Learning Systems](https://mlsanity.com/paper/2301.07104/) (2023)
- [Auto-encoding GPS data to reveal individual and collective behaviour](https://mlsanity.com/paper/2312.00456/) (2023)
- [Spatial Clustering Approach for Vessel Path Identification](https://mlsanity.com/paper/2403.05778/) (2024)
- [Spatial Temporal Attention based Target Vehicle Trajectory Prediction for Internet of Vehicles](https://mlsanity.com/paper/2501.00890/) (2025)
- [GSMT: Graph Fusion and Spatiotemporal TaskCorrection for Multi-Bus Trajectory Prediction](https://mlsanity.com/paper/2508.09227/) (2025)
- [Multi-vessel Interaction-Aware Trajectory Prediction and Collision Risk Assessment](https://mlsanity.com/paper/2509.01836/) (2025)

## Abstract (arXiv)

Data-driven methods open up unprecedented possibilities for maritime surveillance using Automatic Identification System (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation of results shows: i) the superiority of attention pooling over static pooling for the specific application, and ii) the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS.