A Comparison of Trajectory Compression Algorithms Over AIS Data

Abstract

Today's industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. Trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compression algorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.

Ship trajectory data compression based on SH-DP algorithm

“…AIS data is divided into real-time data and historical data based on the obtained time, and ship trajectory data compression algorithms are mainly divided into two categories: offline compression and online compression [2] . Offline compression is performed after completing AIS data collection and discarding some redundant points.…”

A Flexible Trajectory Compression Algorithm for Multi-Modal Transportation

“…Therefore, storing, retrieving, managing, processing, and querying these trajectories is computationally expensive and requires large storage spaces (Nasiri, Azimi, and Abbaspour, 2018;Zhao and Shi, 2019). A critical step in trajectory representation and preprocessing is to reduce incoming data volumes while preserving the main properties and semantics associated with these trajectories and resulting movement patterns (Makris, Kontopoulos, Alimisis, and Tserpes, 2021). Over the past few years, several compression methods have been proposed to reduce the volume of trajectory data.…”

A trajectory data compression algorithm based on spatio-temporal characteristics

“…Trajectory data are also widely used for many purposes. The trajectory data of various vehicles can reflect the operational status of urban traffic, which can help traffic management departments to optimize road facilities and plan traffic lines ( Chen et al, 2020a ; Makris et al, 2021a ; Li et al, 2020 ; Chen et al, 2020b ). Pedestrian trajectory data can be used to analyze restaurants and tourist areas that are of interest to pedestrians, as well as to assist shops in choosing business locations ( Jiagao et al, 2015 ; Ji et al, 2016 ; Kang et al, 2017 ).…”