Most global trade statistics in the public domain refer to official customs data, which are not generally available on a micro (individual cargo) level. With the increasing availability and completeness of ship positioning data from the global Automated Identification System (AIS), it is possible to derive more timely and detailed trade statistics for homogeneous commodity groups. The objective of this article is twofold: (1) to compare the accuracy of AIS-derived trade statistics to official customs data in the crude oil market and (2) to add a breakdown of trade by vessel size over time. We find that while AIS-derived data for seaborne crude exports show good alignment with official export numbers in aggregate, there are substantial temporal and geographical differences across countries and time due to the use of pipelines and transshipment in parts of the supply chain. We highlight the challenges in properly structuring and aggregating micro-level cargo data. Our findings are important for the proper derivation of shipping demand from trade data.

The importance of a seaport depends on how well it is connected in a transportation network. A port’s connectivity is therefore one of the key issues in determining its competitiveness and developments in regions and countries. We construct a port connectivity index for major Norwegian ports based on a unique dataset derived from the automated identification system (AIS) for multiple vessel types over a 7-year period. Port connectivity is evaluated empirically by the number of unique vessel visits, vessel sizes, and cargo sizes. The research has implications for port authorities and policy makers in the areas of port planning, infrastructure investment, short sea shipping promotion, and environmental policies. The contributions of this research are twofold. First, the methodology linking the AIS vessel-tracking system with port connectivity is a pioneering empirical application of maritime big data. Second, the port connectivity index is constructed for multiple vessel types and regional port groups, which is an improvement from the current literature where conceptual measures are constructed based on hypothetical and usually too simple optimization rules. The methodology can be easily expanded to other regions in the world.

Port congestion is an obstacle to smooth energy supply chain management. This research identifies and quantifies the economic implications of congestion in a real-time framework, in which the temporal and geospatial port congestion status is analysed using the vessel tracking information captured by satellites, the Automatic Identification System. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed to automatically identify vessel clusters at ports and quantify port turnaround time, and thus real-time port congestion status. A framework is then outlined to analyze the economic implications of congestion for various stakeholders in the system. A case study is conducted on Indian LPG ports, where congestion frequently occurs. The analysis has important implications for the industrial participants engaged in energy transportation to assess the impact, and for policymakers in a better network design.