Abstract

In tramp shipping, a preliminary route is required for voyage planning at the pre-fixture stage (before a chartering contract is agreed). Such routes are conventionally designated by using pilot charts or software considering long-term statistical weather. However, it has been experienced by tramp operators that such route solutions often poorly estimated sailing distances for long journeys and thereby cause inappropriate cost estimation and bad voyage plan. To fill this gap, a data-driven methodology is proposed in this paper to establish a practical route library with the consideration of ship sizes, load conditions and seasonality. In this method, it first requires a dividing of ship trajectories into local sea passage and open sea passage. The voyage trajectories made of AIS points are then simplified to pattern nodes based on a speed-weighted geolocation method. Afterwards, the KMeans algorithm is deployed to properly classify these pattern nodes, identifying the most representative nodes (routes) in open sea passages. Simultaneously, the connection points are identified by DBSCAN algorithm, representing local sea passages. Combining the representative routes in open sea passages and the connection points in local sea passages, the most navigated routes between two ports are obtained. Finally, case studies are conducted for the Pacific Ocean and the Atlantic Ocean respectively using global AIS data from tanker vessels to demonstrate the feasibility and effectiveness of this methodology. The proposed route library is capable of providing reliable route references to support the decision-making at the pre-fixture stage.

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