An adaptive trajectory compression and feature preservation method for maritime traffic analysis

Ship trajectory data extracted from Automatic Identification System (AIS) has been extensively used for maritime traffic analysis. Yet the enormous volume of AIS data has come with substantial challenges related to storing, processing, analyzing, transmitting, and transferring. Trajectory compressio...

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Veröffentlicht in:Ocean engineering Jg. 312; S. 119189
Hauptverfasser: Guo, Shaoqing, Bolbot, Victor, Valdez Banda, Osiris
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.11.2024
Schlagworte:
Adaptive ship trajectory compression
AIS data
Data-driven analysis
Douglas-peucker algorithm
Feature preservation
Top-down kinematic compression
AIS data
Feature preservation
Data-driven analysis
Adaptive ship trajectory compression
Top-down kinematic compression
Douglas-peucker algorithm
ISSN:0029-8018
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Zusammenfassung:Ship trajectory data extracted from Automatic Identification System (AIS) has been extensively used for maritime traffic analysis. Yet the enormous volume of AIS data has come with substantial challenges related to storing, processing, analyzing, transmitting, and transferring. Trajectory compression techniques have been widely investigated to remedy the challenge. However, conventional compression techniques such as Douglas-Peucker (DP) algorithm mainly depend on line simplification algorithms, falling short in accurately identifying and preserving crucial information within trajectories. Moreover, using kinematic information from AIS data has posed difficulties associated with compression threshold determination. Hence, an adaptive method capable of considering multiple information from AIS is required. In this paper, a Top-Down Kinematic Compression (TDKC) algorithm aimed at adaptive trajectory compression and feature preservation is proposed. By incorporating time, position, speed, and course attributes from AIS data, TDKC exploits a Compression Binary Tree (CBT) method to address the recursion termination problem and determine the threshold automatically. A case study was conducted to evaluate the performance of TDKC using AIS data from Gulf of Finland, where a comparison with conventional algorithms and their improved versions based on specific performance evaluation metrics was involved. The results demonstrate TDKC's superiority in facilitating maritime traffic analysis. •Develop a novel adaptive ship trajectory compression and feature preservation method that incorporates kinematic information from AIS data.•Propose Synchronous Velocity Difference (SVD) to enhance information difference measurement.•Present the concept of Compression Binary Tree (CBT) to solve the recursion termination problem and enable adaptive threshold-setting.•Introduce a velocity-based similarity metric to fill the gap in evaluating velocity information preservation.•The proposed method is compared to 7 other well-known methods to demonstrate its superiority.
ISSN:0029-8018
DOI:10.1016/j.oceaneng.2024.119189