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.

翻译：运动轨迹分类在交通领域具有广泛应用，是支撑大规模运动轨迹生成与异常检测的关键技术，对于灾后或其他外部冲击下的安全应用至关重要。然而，当前最先进的方法基于监督式深度学习——当轨迹分布因外部冲击发生变化时，这类方法面临显著挑战。本文提出一种神经符号混合的规则化框架，用于实现错误检测与校正，并将其集成至我们的运动轨迹平台。我们在多个最新SOTA模型上进行了系列实验，结果表明：该框架能实现高精度错误检测（预测错误的F1分数最高达0.984），在测试分布变化时提升模型准确率（零样本准确率较SOTA提升8.51%），并在基础应用场景中超越SOTA模型的准确率。此外，我们提供了一系列指导算法开发的理论性质分析。