A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks

Abstract--We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportatio n networks. The proposed EVT -LSTM model is derived from the popular LSTM (Long Short-T erm Memory) network and adopts an objective function that is based on fundamental results f rom EVT (Extreme V alue Theory). We compare the EVT -LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diver se real-world data sets demonstrate the superior anomaly dete ction performance of our proposed model over the other models considered in the comparison study. The increasing availability of large-scale traffic data set s provides an opportunity to explore them for knowledge discovery in ITS (Intelligent Transportation Systems). The av - enues for exploration are numerous, ranging from uncoverin g traffic patterns [1], city dynamics [2], driving directions [3], discovering hot spots in a city [4], finding vacant taxis arou nd a city [5], predicting taxi demand [6], taxi operation patte rns [7], to detecting anomalies [8], among others. V arious verticals of ITS have always received active research attention in the past. However, the recent emergence of deep learning techniques and their applicability in tran s-portation systems has resulted in a heightened interest in t his area [9]. Consequently, traditional machine learning mode ls in many applications are now being replaced by deep learning techniques, which is reshaping the landscape of intelligen t transport networks. Out of the several applications of ITS, the area of anomaly detection has benefited significantly from th e application of deep learning-based techniques [10]. Anoma ly detection aims to find those patterns which are not normally expected from the data. Typical observations from traffic da ta demonstrate strong spatiotemporal patterns, showing per iod-icity and strong correlations between adjacent observatio ns.