Cell Grid Architecture for Maritime Route Prediction on AIS Data Streams

TL;DR

This paper presents a scalable cell grid architecture using hash tables for accurate maritime route prediction from AIS data streams, with configurable parameters and semi-supervised learning capabilities.

Contribution

It introduces a novel cell grid architecture tailored for AIS data stream prediction, enhancing accuracy and scalability with configurable tuning and semi-supervised learning.

Findings

High prediction accuracy on AIS data streams

Scalable performance demonstrated

Configurable architecture with semi-supervised learning

Abstract

The 2018 Grand Challenge targets the problem of accurate predictions on data streams produced by automatic identification system (AIS) equipment, describing naval traffic. This paper reports the technical details of a custom solution, which exposes multiple tuning parameters, making its configurability one of the main strengths. Our solution employs a cell grid architecture essentially based on a sequence of hash tables, specifically built for the targeted use case. This makes it particularly effective in prediction on AIS data, obtaining a high accuracy and scalable performance results. Moreover, the architecture proposed accommodates also an optionally semi-supervised learning process besides the basic supervised mode.