Detection of Trajectory Outliers in Intelligent Transportation Systems

Abstract

In this paper, we provide a technique for identifying outliers based on embedding trajectory deviation points and deep clustering. We begin by constructing the network topology and the neighbors of the nodes to create a structural embedding while capturing the interactions of the nodes. We then develop a strategy to determine the hidden representation of distraction points in the road network topology. To create a collection of sequences from a hierarchical multilayer network, a biased random walk is used. This sequence is used to fine tune the embedding of the nodes. The trip embedding was then determined by averaging the node embedding values. Finally, the embeddings are clustered using an LSTM-based pairwise classification strategy based on similarity metrics. The experimental results show that compared to the generic techniques Node2Vec and Struct2Vec, the proposed embedding learning trajectory captures the structural identity and improves the F-measure by 5.06% and 2.4%, respectively.