Abstract
The shipping company or the operator determines the mode of operation of a ship. In the case of container ships, there may be various operating patterns employed to arrive at the destination within the stipulated time. In addition, depending on the influence of the ocean’s environmental conditions, the speed and the route can be changed. As the ship’s fuel oil consumption is closely related to its operational pattern, it is possible to identify the most economical operations by analyzing the operational patterns of the ships. The operational records of each shipping company are not usually disclosed, so it is necessary to estimate the operational characteristics from publicly available data such as the automatic identification system (AIS) data and ocean environment data. In this study, we developed a visualization program to analyze the AIS data and ocean environmental conditions together and propose two categories of applications for the operational analysis of container ships using maritime big data. The first category applications are the past operation analysis by tracking previous trajectories, and the second category applications are the speed pattern analysis by shipping companies and shipyards under harsh environmental conditions. Thus, the operational characteristics of container ships were evaluated using maritime big data.
Highlights
In global supply chains, maritime transport has the largest volume of trade
We proposed past trajectory tracking, the speed comparison of different container ships for the same route, operation comparison among series ships for the same route, and mooring or anchoring time analysis as possible trajectory tracking and analysis applications
The results were obtained by analyzing the maritime big data
Summary
Maritime transport has the largest volume of trade. Bell and. We present a method to streamline container ships’ operations by analyzing maritime big data, such as automatic identification system (AIS) data and ocean environment data. The operational characteristics leveraged from maritime big data can improve the operations of container ships. We combined the two different big data to analyze the operations of container ships. We present the operational analysis methodology of container ships by analyzing the maritime big data using big data technologies.
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