Abstract:

In the background of "Onebelt, Oneroad " maritime strategy in current China, maritime industry is booming, and a lot of new docks are under construction. How to quickly and accurately update the space data of wharfs has a strong practical significance for analyzing foreign trades and improving the efficiency of port services. Artificial means are currently the major approach to update marine charts, but an update interval is usually between 3 and 12 months, which cannot meet the demand. As a new approach to obtain space information of docks, we can apply Including Inmarsat C system, Beidou satellite, Argos satellite and other means to obtain vessel position data for dock mining. We propose a dock mining algorithm based on selfoptimizing parameters DBSCAN (densitybased clustering) for large scale vessel position data from Automatic Identification System (AIS ). On the one hand, the DBSCAN core parameters of different vessel types with different density distributions can be automatically optimized and learnt, and the mooring areas containing docks then can be mined out flexibly. On the other hand, by fusing with the integration of landbased structures and other spatial data, we can exclude anchor zones in mooring regions, berthing areas and etc, with high accuracy. Experiments on Chinese real locus of RoRo real data and the international trajectory data over the past two years (April 2012~April 2014) show that the proposed algorithm can mine docks with an accuracy rate of 93%.

Key words: AIS;vessel location data;DBSCAN algorithm;parameter optimization;docks mining