| Summary: | Over 70% of the total value of international trade is carried by sea, accounting for 80% of all cargo in terms of volume. In 2016, the UN Secretary General drew attention to the role of maritime transport, describing it as “the backbone of global trade and of the global economy”. Maritime trade flows impact not only the economic development of the concerned regions, but also their ecosystems. Moving ships are an important vector of spread for bioinvasions. Shipping routes are constantly evolving and likely to be affected by the consequences of Climate Change, while at the same time ships are a considerable source of air pollution, with CO2 emissions at a level comparable to Germany, and NOx and SOx emissions comparable to the United States. With the development of Arctic shipping becoming a reality, the need to understand the behavior of this system and to forecast future maritime trade flows reasserts itself. Despite their scope and crucial importance, studies of maritime trade flows on a global scale, based on data and formal methods are scarce, and even fewer studies address the question of their evolution. In this thesis we use a unique database on daily movements of the world fleet between 1977 and 2008 provided by the maritime insurer Lloyd’s in order to build a complex network of maritime trade flows where ports stand for nodes and links are created by ship voyages. In this thesis we perform a data-driven analysis of the maritime trade network. We use tools from Complexity Science and Machine Learning applied on network data to study the network’s properties and develop models for predicting the opening of new shipping lines and for forecasting future trade volume on links. Applying Machine Learning to analyse networked trade flows appears to be a new approach with respect to the state-of-the-art, and required careful selection and customization of existing Machine Learning tools to make them fit networked data on physical flows. The results of the thesis suggest a hypothesis of trade following a random ...
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