  Vessel trajectory clustering, a crucial component of the maritime intelligent
transportation systems, provides valuable insights for applications such as
anomaly detection and trajectory prediction. This paper presents a
comprehensive survey of the most prevalent distance-based vessel trajectory
clustering methods, which encompass two main steps: trajectory similarity
measurement and clustering. Initially, we conducted a thorough literature
review using relevant keywords to gather and summarize pertinent research
papers and datasets. Then, this paper discussed the principal methods of data
pre-processing that prepare data for further analysis. The survey progresses to
detail the leading algorithms for measuring vessel trajectory similarity and
the main clustering techniques used in the field today. Furthermore, the
various applications of trajectory clustering within the maritime context are
explored. Finally, the paper evaluates the effectiveness of different algorithm
combinations and pre-processing methods through experimental analysis, focusing
on their impact on the performance of distance-based trajectory clustering
algorithms. The experimental results demonstrate the effectiveness of various
trajectory clustering algorithms and notably highlight the significant
improvements that trajectory compression techniques contribute to the
efficiency and accuracy of trajectory clustering. This comprehensive approach
ensures a deep understanding of current capabilities and future directions in
vessel trajectory clustering.
