  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 paper 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 GPU architecture. Comprehensive
experiments on trajectory compression and visualization have been conducted on
large-scale AIS data of recording ship movements collected from 3 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; (3) the Gaussian kernel was capable of generating more
appropriate KDE-based visualization performance by comparing with other seven
kernel functions.
