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

TL;DR

This paper presents a neural network-based model for forecasting vessel AIS message content, effectively handling irregular temporal data and improving route prediction accuracy for multiple vessels simultaneously.

Contribution

It introduces a neural network approach that models AIS transmission behavior, addressing irregularities and enhancing vessel movement forecasting in noisy data environments.

Findings

Deep learning models effectively preserve spatial awareness despite irregular data.

Convolutional, feed-forward, and recurrent layers improve forecasting performance.

Model achieved up to 38% accuracy improvement in vessel route prediction.

Abstract

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.