Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification
This addresses reliability issues in trajectory classification for transportation safety applications during disasters, representing a domain-specific incremental improvement.
The paper tackles the problem of movement trajectory classification models failing when trajectory distributions change due to external shocks, by developing a neuro-symbolic rule-based framework for error detection and correction. The results include F1 scores up to 0.984 for error prediction, 8.51% improvement in zero-shot accuracy over SOTA for out-of-distribution data, and accuracy improvements over SOTA models.
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.