From ports to routes: Extracting multi-scale shipping networks using massive AIS data

Abstract

Maritime transportation is a critical component of global trade and commerce. To ensure maritime safety, fixed shipping routing has been established in many complex waters. However, there is currently a lack of comprehensive digital shipping networks in wide-range maritime areas. To better understand the navigational patterns, this paper proposes a data-driven extraction framework for multi-scale shipping networks, including port-, node-, and route-level shipping networks. It is essentially a hierarchical approach, which progresses from port to route. In particular, for the extraction of port-level shipping networks, the clustering in quest (CLIQUE) and alpha-shapes algorithms are employed to accurately extract the boundaries and spatial extents of individual ports. For the node-level shipping network extraction, an adaptive Douglas-Peucker algorithm is developed to identify crucial feature points, and CLIQUE clustering is further exploited to extract the network waypoints. A novel slice-based traffic flow fitting algorithm is finally introduced to extract the route-level shipping network. To verify the performance of shipping network extraction methods, comprehensive experiments are conducted using the massive Automatic Identification System (AIS) data in different water areas. The experimental results have demonstrated that our method was capable of extracting multi-scale shipping networks, revealing traffic characteristics and vessel behaviours. Overall, the method proposed herein is useful for shipping logistic analysis and provides a foundation for several potential maritime applications, including route planning, trajectory prediction, and others.