Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning
This work addresses vessel route prediction for maritime monitoring applications, but it is incremental as it primarily combines existing neural network components.
This paper tackles the problem of forecasting vessel trajectories from noisy AIS data with irregular transmission intervals by developing a neural network model that combines convolutional, feed-forward, and recurrent layers. The model achieved Relative Percentage Difference scores of 36%, 37%, and 38% across different sequence lengths, outperforming baseline RNN variants like Elman's RNN (92%, 45%, 96%), GRU (51%, 52%, 40%), and LSTM (129%, 98%, 61%).
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.