Risk Analysis and Quantification of Vulnerability in Maritime Transportation Network Using AIS Data
The risk analysis and vulnerability quantification in the global maritime transportation networks are important to maintain the healthy economy in today’s world. In this paper, we analyze the auto identification system (AIS) data that provides us with the real-time location of vessels. The AIS data of a Japanese company was used to compute the throughputs of the ports for the vessel it operates and the topology of the global maritime transportation network during a certain time period. Firstly, we computed the conventional un-weighted node-level characteristics and compared it with the port throughput. This comparison shows the statistically significant correlations, especially, with the in-degree and the Page-Rank. Secondly, we modeled and simulate to quantify the vulnerability and importance of each port identified from the AIS data. The simulation results indicate that Singapore is the most robust and influential port when disrupted. In addition, we introduce a method to compute the vulnerability and importance analytically. Subsequent research will be required to extend the proposed analysis to the complete data sets for all cargo-ships and utilize the high performance computing technologies to accelerate the computation.
KeywordsAutomatic identification system AIS Complex network analysis Open data analysis Risk assessment Maritime transportation network
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