# Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning

arXiv: https://arxiv.org/abs/2202.13867 · HTML: https://mlsanity.com/paper/2202.13867/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Highway Networks](https://mlsanity.com/paper/1505.00387/) (2015)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Recurrent Highway Networks](https://mlsanity.com/paper/1607.03474/) (2016)
- [Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks](https://mlsanity.com/paper/2101.02486/) (2021)
- [Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning](https://mlsanity.com/paper/2008.12833/) (2020)
- [A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams](https://mlsanity.com/paper/1806.03972/) (2018)
- [Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning](https://mlsanity.com/paper/2004.03722/) (2020)
- [3D Density Histograms for Criteria-driven Edge Bundling](https://mlsanity.com/paper/1504.02687/) (2015)

## Built on by (most cited first, 3 of 3)

- [Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting](https://mlsanity.com/paper/2310.18948/) (2023)
- [Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models](https://mlsanity.com/paper/2401.13098/) (2024)
- [A Systematic Approach for Selecting Trajectories for Data Augmentation](https://mlsanity.com/paper/2606.10938/) (2026)

## Abstract (arXiv)

The oceans are a source of an impressive mixture of complex data that could be used to uncover relationships yet to be discovered. Such data comes from the oceans and their surface, such as Automatic Identification System (AIS) messages used for tracking vessels' trajectories. AIS messages are transmitted over radio or satellite at ideally periodic time intervals but vary irregularly over time. As such, this paper aims to model the AIS message transmission behavior through neural networks for forecasting upcoming AIS messages' content from multiple vessels, particularly in a simultaneous approach despite messages' temporal irregularities as outliers. We present a set of experiments comprising multiple algorithms for forecasting tasks with horizon sizes of varying lengths. Deep learning models (e.g., neural networks) revealed themselves to adequately preserve vessels' spatial awareness regardless of temporal irregularity. We show how convolutional layers, feed-forward networks, and recurrent neural networks can improve such tasks by working together. Experimenting with short, medium, and large-sized sequences of messages, our model achieved 36/37/38% of the Relative Percentage Difference - the lower, the better, whereas we observed 92/45/96% on the Elman's RNN, 51/52/40% on the GRU, and 129/98/61% on the LSTM. These results support our model as a driver for improving the prediction of vessel routes when analyzing multiple vessels of diverging types simultaneously under temporally noise data.