This hands-on material concerns research techniques that may be used for mobility tracking over trajectories of vessels monitored over a large maritime area. The focus is on maintaining summarized representations of such trajectories in online fashion based on surveillance data streams broadcast through Automatic Identification System (AIS) and concerning positions from a fleet of numerous vessels.

We provide source code of several generic, state-of-the-art trajectory simplification algorithms that can offer concise summaries of each trajectory as it evolves. Instead of retaining every incoming position, such methods drop any predictable positions along trajectory segments of normal motion characteristics with minimal loss in accuracy. Note that each algorithm specifies its own parametrization, which may require careful fine-tuning depending on the characteristics of the available AIS data.

This video simulates the simplified synopsis of a vessel trajectory sailing from the port of Thessaloniki, moving across the Aegean Sea, and finally reaching Rhodes. Note that this simplification algorithm also identifies critical points, i.e., it annotates each retained point in this synopsis with a particular characterization (stop, turn, slow motion, etc.) that captures important changes across the route in online fashion:

It should be stressed that each algorithm is sensitive to proper choice of parameter values, which also differ amongst algorithms. Fixed parametrizations applicable to any dataset are rather difficult to specify, as setting suitable parameter values requires careful data exploration.

Execution results from each algorithm include the simplified trajectory (in CSV format), as well as a number of statistics printed in the terminal:

• Execution time (in milliseconds) measures the performance efficiency of the method. Note that methods are developed in different programming frameworks and languages, hence their comparison in terms of execution time would not be fair.
• Compression ratio is the percentage (%) of locations dropped from the simplified representation compared to the positions available in the original trajectory. Values closer to 0 indicate a poor compression, whereas values closer to 1 signify a more aggressive compression (i.e., light-weight simplification results).
• Root Mean Square Error (RMSE) measures the approximation error between the original and simplified sequences of locations for a given trajectory. The lesser this error, the less the simplified trajectory deviates from the original one all along its course.

The resulting CSV files with the simplified trajectories can be visualized in GIS (e.g., in QGIS), in order to be comparatively inspected against the original vessel tracks.