An automatic algorithm for generating seaborne transport pattern maps based on AIS

Abstract

We provide an automatic algorithm proceeding from raw automatic identification system (AIS) data, through filters, highlighting vessel types, cargo, or regions, to discrete transport patterns, describing the geographic, directional, and total volume of transport. The designed algorithm automatically detects major ports or zones, where vessels are stationary and close to land, and groups them hierarchically in order to determine the end nodes in a transport matrix. In conjunction with vessel type, size, and draught information, the transport matrix is then used to compile transported tonnage and vessel count, to display as trade flows between nodes. In addition, by clicking on individual routes, the system also provides quantitative details, such as summed tonnage, or passing vessel counts. We use trades carried out by VLCC and Supramax bulk carriers as examples to demonstrate the results. The contribution of the research is threefold. First, we use AIS data to aggregate “real-time” trade flows. This substantially reduces latency from the traditional way of mapping flows and generating trade statistics. Second, the ability to visualize changes in seasonal or regional trading patterns enables economists and policy makers to monitor changes at a macro level. Finally, the ability to dripple down to the individual ship level allows commercial traders to monitor vessels or company performances, which provides valuable information in trading and decision-making.