Extracting Global Shipping Networks from Massive Historical Automatic Identification System Sensor Data: A Bottom-Up Approach

Abstract

The increasing availability of big Automatic Identification Systems (AIS) sensor data offers great opportunities to track ship activities and mine spatial-temporal patterns of ship traffic worldwide. This research proposes a data integration approach to construct Global Shipping Networks (GSN) from massive historical ship AIS trajectories in a completely bottom-up way. First, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is applied to temporally identify relevant stop locations, such as marine terminals and their associated events. Second, the semantic meanings of these locations are obtained by mapping them to real ports as identified by the World Port Index (WPI). Stop events are leveraged to develop travel sequences of any ship between stop locations at multiple scales. Last, a GSN is constructed by considering stop locations as nodes and journeys between nodes as links. This approach generates different levels of shipping networks from the terminal, port, and country levels. It is illustrated by a case study that extracts country, port, and terminal level Global Container Shipping Networks (GCSN) from AIS trajectories of more than 4000 container ships in 2015. The main features of these GCSNs and the limitations of this work are finally discussed.

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“…In parallel, engineers, economists, and geographers gradually adopted such a framework, mainly confirming the already observed scale-free and small-world macro-structure (Ducruet and Notteboom, 2012;Hu and Zong, 2013;Kang et al, 2014;Liu et al, 2018;Pan et al, 2019). Some of them discussed the effect of node aggregation at different scales (Tsiotas and Polyzos, 2018;Wang et al, 2019a), while others studied the global maritime network with the world region as the unit of analysis (Tran and Haasis, 2014;Li et al, 2015;Xu et al, 2015). A large body of literature focused on ranking ports by centrality indicators, introducing more or less novelty (Wang and Cullinane, 2008;Lam and Yap, 2011;Montes et al, 2012;Cullinane and Wang, 2012;Doshi et al, 2012;Freire Seoane et al, 2013;Wang and Cullinane, 2014;Kang et al, 2014;Bartholdi et al, 2016).…”

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