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Trajectory Compression Algorithm via Geospatial Background Knowledge
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AI
DOI：10.3390/jmse13030406.png)
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Abstract
En 中文
The maritime traffic status is monitored through the Automatic Identification System (AIS) installed on vessels. AIS data record the trajectory of each ship. However, due to the short sampling interval of AIS data, there is a significant amount of redundant data, which increases storage space and reduces data processing efficiency. To reduce the redundancy within AIS data, a compression algorithm is necessary to eliminate superfluous points. This paper presents an offline trajectory compression algorithm that leverages geospatial background knowledge. The algorithm employs an adaptive function to preserve points characterized by the highest positional errors and rates of water depth change. It segments trajectories according to their distance from the shoreline, applies varying water depth change rate thresholds depending on geographical location, and determines an optimal distance threshold using the average compression ratio score. To verify the effectiveness of the algorithm, this paper compares it with other algorithms. At the same compression ratio, the proposed algorithm reduces the average water depth error by approximately 99.1% compared to the Douglas-Peucker (DP) algorithm, while also addressing the common problem of compressed trajectories potentially intersecting with obstacles in traditional trajectory compression methods.
Keywords:
AIS data
trajectory compression
DP algorithm
geospatial background
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