Within the last years the shipping industry invest-ments continue to grow to improve maritime transport systems. A vital part of the maritime transport systems is the accurate Vessel Traffic Flow Forecasting (VTFF). In this paper, we approach the VTFF problem from two different perspectives: a) indirect - as a vessel route forecasting application via employing predicted vessels locations in the future, and b) direct - as a flow sequence forecasting problem. In both strategies, machine learning methods are employed because they can leverage from the massive vessel surveillance information to enable deeper digitalization in the shipping industry. This work performs an experimental comparative study between the two approaches over a real dataset from the maritime domain.

The massive-scale data generation of positioning (tracking) messages, collected by various surveillance means, has posed new challenges in the field of mobility data analytics in terms of extracting valuable knowledge out of this data. One of these challenges is online cluster analysis, where the goal is to unveil hidden patterns of collective behaviour from streaming trajectories, such as co-movement and co-stationary (aka anchorage) patterns. Towards this direction, in this paper, we demonstrate MaSEC (Moving and Stationary Evolving Clusters), a system that discovers valuable behavioural patterns as above. In particular, our system provides a unified solution that discovers both moving and stationary evolving clusters on streaming vessel position data in an online mode. The functionality of our system is evaluated over two real-world datasets from the maritime domain.