GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries

Abstract

The automatic identification system (AIS), an automatic vessel-tracking system, has been widely adopted to perform intelligent traffic management and collision avoidance services in maritime Internet-of-Things (IoT) industries. With the rapid development of maritime transportation, tremendous numbers of AIS-based vessel trajectory data have been collected, which make trajectory data compression imperative and challenging. This article mainly focuses on the compression and visualization of large-scale vessel trajectories and their graphics processing unit (GPU)-accelerated implementations. The visualization was implemented to investigate the influence of compression on vessel trajectory data quality. In particular, the Douglas–Peucker (DP) and kernel density estimation (KDE) algorithms, respectively, utilized for trajectory compression and visualization, were significantly accelerated through the massively parallel computation capabilities of the GPU architecture. Comprehensive experiments on trajectory compression and visualization have been conducted on large-scale AIS data of recording ship movements collected from three different water areas, i.e., the South Channel of Yangtze River Estuary, the Chengshan Jiao Promontory, and the Zhoushan Islands. Experimental results illustrated that: 1) the proposed GPU-based parallel implementation frameworks could significantly reduce the computational time for both trajectory compression and visualization; 2) the influence of compressed vessel trajectories on trajectory visualization could be negligible if the compression threshold was selected suitably; and 3) the Gaussian kernel was capable of generating more appropriate KDE-based visualization performance by comparing with other seven kernel functions.