  Using data sources beyond the Automatic Identification System to represent
the context a vessel is navigating in and consequently improve situation
awareness is still rare in machine learning approaches to vessel trajectory
prediction (VTP). In inland shipping, where vessel movement is constrained
within fairways, navigational context information is indispensable. In this
contribution targeting inland VTP, Gaussian Mixture Models (GMMs) are applied,
on a fused dataset of AIS and discharge measurements, to generate multi-modal
distribution curves, capturing typical lateral vessel positioning in the
fairway and dislocation speeds along the waterway. By sampling the probability
density curves of the GMMs, feature vectors are derived which are used,
together with spatio-temporal vessel features and fairway geometries, as input
to a VTP transformer model. The incorporation of these distribution features of
both the current and forthcoming navigation context improves prediction
accuracy. The superiority of the model over a previously proposed transformer
model for inland VTP is shown. The novelty lies in the provision of
preprocessed, statistics-based features representing the conditioned spatial
context, rather than relying on the model to extract relevant features for the
VTP task from contextual data. Oversimplification of the complexity of inland
navigation patterns by assuming a single typical route or selecting specific
clusters prior to model application is avoided by giving the model access to
the entire distribution information. The methodology's generalizability is
demonstrated through the usage of data of 3 distinct river sections. It can be
integrated into an interaction-aware prediction framework, where insights into
the positioning of the actual vessel behavior in the overall distribution at
the current location and discharge can enhance trajectory prediction accuracy.
