Abstract
The trajectory data of vessel AIS (automatic identification system) has important theoretical and application value for information supporting decisions. However, large sizes lead to difficulties in storing, querying, and processing. To solve the problems of high compression ratio and longtime consumption of the existing online trajectory compression algorithm, an SPM (scan-pick-move) trajectory data compression algorithm added sliding window is proposed. In order to better compress vessel trajectory data regarding compression efficiency, the sliding window is added to the classical SPM algorithm. In order to reduce trajectory data storage space, the maximum offset distance reference trajectory point is used as the criterion of whether the current trajectory point can be compressed. In this paper, the multi-dimensional space-time characteristics of trajectory data, such as distance error, compression ratio and compression time, are selected to evaluate the trajectory compression method from three levels: geometric characteristics, motion characteristics and compression efficiency. Compared with the existing SPM trajectory data compression algorithm, parallel experiments are conducted based on AIS data gathered over the duration of a month in the Japan Osaka Bay. The SPM trajectory compression algorithm added sliding window can significantly reduce the compression time and outperforms other existing trajectory compression algorithms in term of average compression error at high compression strengths. Also, the proposed method has high compression efficiency in the range of commonly used compression thresholds.
Highlights
The automatic identification system (AIS) is becoming more and more convenient and efficient in ship navigation, monitoring and traffic flow research
By introducing the maximum offset distance point Pm as the reference trajectory point of the SPM algorithm, this paper provides the discrimination condition of feature point selection, and sets the corresponding threshold δ of trajectory compression distance according to the actual situation
The single vessel trajectory is the AIS data randomly selected from a ship in the data set between 2018-01-01,00:00:00 and 2018-02-01,00:00:00, and the ship’s MMSI (Maritime Mobile Service Identify) is 431000331
Summary
The automatic identification system (AIS) is becoming more and more convenient and efficient in ship navigation, monitoring and traffic flow research. According to SOLAS Convention, since 2002, more ships have been forced to install AIS equipment [1]. With the increase of the number of shipborne terminals, the frequent transmission of equipment information and the improvement of the collection base station, a large number of vessels AIS trajectory data have been generated. Such abundant AIS data can open up various. A common task for all the AIS data studies corresponds to preprocessing massive historical records. Most information in the raw AIS data is redundant in AIS trajectories that consist of massive similar trajectory points.
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