Maritime Pattern Extraction from AIS Data Using a Genetic Algorithm

Abstract

The long term prediction of maritime vessels' destinations and arrival times is essential for making an effective logistics planning. As ships are influenced by various factors over a long period of time, the solution cannot be achieved by analyzing sailing patterns of each entity separately. Instead, an approach is required, that can extract maritime patterns for the area in question and represent it in a form suitable for querying all possible routes any vessel in that region can take. To tackle this problem we use a genetic algorithm (GA) to cluster vessel position data obtained from the publicly available Automatic Identification System (AIS). The resulting clusters are treated as route waypoints (WP), and by connecting them we get nodes and edges of a directed graph depicting maritime patterns. Since standard clustering algorithms have difficulties in handling data with varying density, and genetic algorithms are slow when handling large data volumes, in this paper we investigate how to enhance the genetic algorithm to allow fast and accurate waypoint identification. We also include a quad tree structure to preprocess data and reduce the input for the GA. When the route graph is created, we add post processing to remove inconsistencies caused by noise in the AIS data. Finally, we validate the results produced by the GA by comparing resulting patterns with known inland water routes for two Dutch provinces.

Multi-Label Classification for AIS Data Anomaly Detection Using Wavelet Transform

“…Trajectory is described as a graph; its vertices are the waypoints and its edges are the stages of the actual ship trajectory. Works that propose methods of describing vessels' movement utilizing waypoints are, for example, [7] (using genetic algorithm) or [31].…”

Operational Analysis of Container Ships by Using Maritime Big Data

“…To predict the destination and arrival time, Dobrkovic et al [14] proposed the maritime waypoint discovery method from the AIS data by using machine learning. Furthermore, the same authors [15] proposed a maritime pattern extraction method from the AIS data using the genetic algorithm. Xiao et al [16] simulated ship traffic behavior using the AIS data.…”

Maritime pattern extraction and route reconstruction from incomplete AIS data

“…Upon close inspection of the missed routes, we found the spread of waypoints to be correct in the large segment of the lane, yet the low number of epochs prevented the algorithm to accurately extract waypoints near the starts, and the ends, hence impacting the score. All tests (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) show that increasing the population size has negligible effect on the overall score, and this parameter is removed from further considerations. With tests 4-9, increasing the number of epochs to 800, and finally to 2000, allowed the algorithm to fit all the waypoints, and correctly identify all the lanes.…”

“…For Dutch logistics service providers (LSPs), it is essential to maximize the utilization of inland water transportation resources. The most important such resource of LSPs are barges that in an This paper is the extended version of the DSAA'2016 special session paper "Maritime Pattern Extraction from AIS data Using a Genetic Algorithm" [1]. LSPs define terminal disturbances as events when a deep sea vessel makes an unscheduled arrival at the port terminal [2].…”