Cell Grid Architecture for Maritime Route Prediction on AIS Data Streams
This work addresses maritime traffic prediction for naval applications, but it is incremental as it adapts existing methods to a specific use case.
The paper tackled the problem of accurate maritime route prediction on AIS data streams by proposing a cell grid architecture based on hash tables, achieving high accuracy and scalable performance.
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.