Using neural networks to model the behavior in vessel trajectories

Abstract

The contemporary trend shows a shift from rule-based algorithms to deep learning. In the last few years, this field has been developing rapidly and its popularity has increased to a large extent. This happened for a good reason, since deep learning was able to solve some of the hardest problems in fields like image recognition, natural language processing and speech recognition. A large proportion of its success has to be credited to the explosion of big data in the past two decades. Structured data sets are essential to deep learning systems. The rising amount of automatic identification system data is a key example in the current big data boom. The automatic identification system produces spatiotemporal movement data of vessels. It was designed as a collision avoidance system, but researchers have been looking into ways to leverage its data for other tasks. Analyzing behavior in the movement data of vessels can help policymakers and monitoring operators with decision making processes. Improving these processes lead to safer and more resilient marine environments. Unfortunately, the possibility of applying deep learning on vessel movement data is an underexposed topic. This project attempts to explore the research gap in this topic. The objective is therefore to give an overview of the possibilities, complications and opportunities given the current state of the art. Ultimately, this project may serve as a rough guide for those who wish to explore the crossings where deep learning and vessel movement data meet.