  Understanding and representing traffic patterns are key to detecting
anomalous trajectories in the transportation domain. However, some trajectories
can exhibit heterogeneous maneuvering characteristics despite confining to
normal patterns. Thus, we propose a novel graph-based trajectory representation
and association scheme for extraction and confederation of traffic movement
patterns, such that data patterns and uncertainty can be learned by deep
learning (DL) models. This paper proposes the usage of a recurrent neural
network (RNN)-based evidential regression model, which can predict trajectory
at future timesteps as well as estimate the data and model uncertainties
associated, to detect maritime anomalous trajectories, such as unusual vessel
maneuvering, using automatic identification system (AIS) data. Furthermore, we
utilize evidential deep learning classifiers to detect unusual turns of vessels
and the loss of transmitted signal using predicted class probabilities with
associated uncertainties. Our experimental results suggest that the graphical
representation of traffic patterns improves the ability of the DL models, such
as evidential and Monte Carlo dropout, to learn the temporal-spatial
correlation of data and associated uncertainties. Using different datasets and
experiments, we demonstrate that the estimated prediction uncertainty yields
fundamental information for the detection of traffic anomalies in the maritime
and, possibly in other domains.
