Trajectory Data Restoring: A Way of Visual Analysis of Vessel Identity Base on OPTICS

Abstract

Automatic identification system (AIS) data is a significant analysis and decision-making basis for maritime situational awareness. Because of particular navigation environment and the vulnerability of AIS equipment onboard, results in the phenomenon that numerous vessels share the same Maritime Mobile Service Identity (MMSI) in the AIS data collected in ocean and inland waterway. This kind of mixed trajectory information dramatically affects the judgement of the maritime manager and supervisors. In this paper, the visual analytics combined with the algorithm named Ordering Points to Identify the Clustering Structure (OPTICS) is adopted to realize the separation of vessels sharing same MMSI, which can help analysts to recognize the vessel trajectory information and assess the risk of marine traffic correctly. Firstly, this paper illustrates the application of OPTICS clustering method based on space-time distance in AIS trajectory separation. Secondly, the display and interaction of trajectory information of Vessels sharing the same MMSI in OpenStreetMap map were introduced. Then visual analysis method is applied to optimize the parameters of the algorithm and display the trajectory separation effect corresponding to different settings. In final, various practical situations are discussed, and the empirical test shows that it is feasible in AIS chaos trajectory separation.