  In previous work we introduced a trajectory detection module that can provide
summarized representations of vessel trajectories by consuming AIS positional
messages online. This methodology can provide reliable trajectory synopses with
little deviations from the original course by discarding at least 70% of the
raw data as redundant. However, such trajectory compression is very sensitive
to parametrization. In this paper, our goal is to fine-tune the selection of
these parameter values. We take into account the type of each vessel in order
to provide a suitable configuration that can yield improved trajectory
synopses, both in terms of approximation error and compression ratio.
Furthermore, we employ a genetic algorithm converging to a suitable
configuration per vessel type. Our tests against a publicly available AIS
dataset have shown that compression efficiency is comparable or even better
than the one with default parametrization without resorting to a laborious data
inspection.
