Using machine learning for unsupervised maritime waypoint discovery from streaming AIS data

Estimating the future position of a deep sea vessel more than 24 hours in advance is a major challenge for Dutch logistics service providers (LSPs). Their unscheduled arrival in ports directly impacts scheduling and waiting times of barges, propagating throughout the entire supply chain network. To help LSPs' planners improve planning operations, we intend to capture the characteristics of maritime routes for a specific region (the North Sea connecting the Netherlands and United Kingdom) in the form of a directed graph, which can be used as a foundation for predicting destination and arrival time of each associated vessel. To create such graph we need an efficient way to extract waypoints for traffic data and this is the problem we will address in this paper. Since LSPs only use publicly available data for arrival estimation, our solution is entirely based on Automatic Identification System (AIS) data. Extracting positional information from AIS, we explore various machine learning approaches to identify clusters. We apply DBSCAN algorithm and show its advantages and disadvantages when used on AIS data. The same process is repeated using meta-heuristics, comparing clustering results generated by a genetic algorithm and by modified ant-colony optimization to those produced by DBSCAN. Finally, we present a hybrid approach and its ability to discover waypoints, highlighting the achieved improvements. To extend the problem, two constraints are added. The first is the requirement to handle large volumes of streaming AIS data on standard PC-based hardware. The second introduces the common situation of "dark areas" in a map due to problems with receiving and transmitting AIS data. The algorithm discovers route waypoints in efficient and effective ways under these constraints.