Embedding geographic information for anomalous trajectory detection

Abstract

Anomalous trajectory detection is a crucial task in trajectory mining fields. Traditional anomalous trajectory detection methods mainly focus on the differences of a new trajectory and the historical trajectory with density and isolation techniques, which may suffer from the following two disadvantages. (1) They cannot capture the sequential information of the trajectory well. (2) They cannot make use of the common information of the trajectory points. To overcome the above shortcomings, we propose a novel method called A nomalous T rajectory D etection using R ecurrent N eural N etwork (ATD − RNN) which characterizes the trajectory with the learned trajectory embedding. The trajectory embedding can capture the sequential information of the trajectory and depict the internal characteristics between abnormal and normal trajectories. In order to learn the high-quality trajectory embedding, we further propose an attention mechanism to aggregate the long sequential information. Furthermore, to alleviate the data sparsity problem, we augment the datasets between a source and a destination by taking the relevant trajectories into consideration simultaneously. Extensive experiments on real-world datasets validate the effectiveness of our proposed methods.