Detecting abnormal behavior in the transportation planning using long short term memories and a contextualized dynamic threshold

Unsupervised anomaly detection in time-series data is crucial for both machine learning research and industrial applications. Over the past few years, the operational efficiencies of logistics agencies have decreased because of a lack of understanding on how best to address potential client requests. However, current anomaly detection approaches have been inefficient in distinguishing normal and abnormal behaviors from high dimensional data. In this study, we aimed to assist decision makers and improve anomaly detection by proposing a Long Short Term Memory (LSTM) approach with dynamic threshold detection. In the proposed methodology, first, data were processed and inputted into an LSTM network to determine temporal dependency. Second, a contextualized dynamic threshold was determined to detect anomalies. To demonstrate the practicality of our model, real operational data were used for evaluation and our model was shown to more accurately detect anomalies, with values of 0.836 and 0.842 for precision and recall, respectively.