  Classification of movement trajectories has many applications in
transportation and is a key component for large-scale movement trajectory
generation and anomaly detection which has key safety applications in the
aftermath of a disaster or other external shock. However, the current
state-of-the-art (SOTA) are based on supervised deep learning - which leads to
challenges when the distribution of trajectories changes due to such a shock.
We provide a neuro-symbolic rule-based framework to conduct error correction
and detection of these models to integrate into our movement trajectory
platform. We provide a suite of experiments on several recent SOTA models where
we show highly accurate error detection, the ability to improve accuracy with a
changing test distribution, and accuracy improvement for the base use case in
addition to a suite of theoretical properties that informed algorithm
development. Specifically, we show an F1 scores for predicting errors of up to
0.984, significant performance increase for out-of distribution accuracy (8.51%
improvement over SOTA for zero-shot accuracy), and accuracy improvement over
the SOTA model.
