Events, such as prolonged congestion in ports or unavailable ship routes in the maritime network, often initiate cascading congestions that block transportation and/or disrupt services over wide areas. Existing traffic flow analysis methods lack the ability to understand the cascading effects of delays in ship routes or how to reduce overall delays in greater maritime areas. Dependency risk graphs have been proposed as a tool for analyzing such cascading events using dependency chains. This paper proposes a risk-based interdependency analysis method capable to detect large-scale traffic congestions between interconnected ports and ship routes in the maritime network and provide solutions to improve flow. Presented dependency risk chains of ports along with graph theory help us analyze ship routes and detect ports that are affected most when other major ports are congested in the maritime network, detect the causes of bottlenecks, and provide valuable info in relieving delays across container ship routes. We apply the proposed method on historical container ship routing data provided by the MarineTraffic company that maintains a comprehensive maritime database worldwide for more than six million users monthly. This application-oriented, interdisciplinary effort culminated in a prototype tool is able to analyze the historical data for container ships in the entire global maritime network and detect congestion dependencies. The tool can be used to identify key shipping routes or ports that: 1) are prone to delays; 2) greatly affect the overall maritime network due to position, connections and risk of congestion; and/or 3) get affected the most by delays in previous route legs.
G. Stergiopoulos, E. Valvis, D. Mitrodimas, D. Lekkas and D. Gritzalis, “Analyzing Congestion Interdependencies of Ports and Container Ship Routes in the Maritime Network Infrastructure,” in IEEE Access, vol. 6, pp. 63823-63832, 2018, doi: 10.1109/ACCESS.2018.2877659.