A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks
This work addresses anomaly detection for transportation systems, but it appears incremental as it builds on existing LSTM and EVT methods.
The authors tackled anomaly detection in transportation networks by proposing an EVT-LSTM model, which outperformed established baselines on seven real-world datasets.
We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Long Short-Term Memory) network and adopts an objective function that is based on fundamental results from EVT (Extreme Value Theory). We compare the EVT-LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diverse real-world data sets demonstrate the superior anomaly detection performance of our proposed model over the other models considered in the comparison study.