Abstract
Large volumes of automatic identification system (AIS) data provide new ideas and methods for ship data mining and navigation behavior pattern analysis. However, large volumes of big data have low unit values, resulting in the need for large-scale computing, storage, and display. Learning efficiency is low and learning direction is blind and untargeted. Therefore, key feature point (KFP) extraction from the ship trajectory plays an important role in fields such as ship navigation behavior analysis and big data mining. In this paper, we propose a ship spatiotemporal KFP online extraction algorithm that is applied to AIS trajectory data. The sliding window algorithm is modified for application to ship navigation angle deviation, position deviation, and the spatiotemporal characteristics of AIS data. Next, in order to facilitate the subsequent use of the algorithm, a recommended threshold range for the corresponding two parameters is discussed. Finally, the performance of the proposed method is compared with that of the Douglas–Peucker (DP) algorithm to assess its feature extraction accuracy and operational efficiency. The results show that the proposed improved sliding window algorithm can be applied to rapidly and easily extract the KFPs from AIS trajectory data. This ability provides significant benefits for ship traffic flow and navigational behavior learning.
Highlights
According to the SOLAS (International Convention for Safety of Life at Sea) convention, since 2002, an increasing number of ships have been required to install automatic identification system (AIS)equipment onboard [1]
The distance thresholds were set as in the range of 0.25–3 times the ship beam with increments of 0.25, and an angle threshold of 4.5◦ was used in the sliding window algorithm for feature extraction
An improved sliding window algorithm that combines the spatiotemporal characteristics of ship AIS trajectory data was developed and tested to extract key feature point (KFP)
Summary
According to the SOLAS (International Convention for Safety of Life at Sea) convention, since 2002, an increasing number of ships have been required to install automatic identification system (AIS)equipment onboard [1]. A large volume of AIS trajectory data has been accumulated owing to the increasing number of ship terminals, the high frequency of ship terminal information forecasts [2], and improved data collection. A large volume of data provides new ideas and methods for the effective application of AIS trajectory data for improving the safety and efficiency of maritime traffic [3,4], which has become an active research topic with the development of big data analysis techniques. The volume, velocity, variety, value, and veracity (5V) characteristics of big data are a double-edged sword; big data can provide several new research directions and ideas, research on hardware devices and algorithms is a significant challenge. The massive volumes cause great difficulties in all aspects of data storage, Sensors 2019, 19, 2706; doi:10.3390/s19122706 www.mdpi.com/journal/sensors
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