Context-Aware Trajectory Anomaly Detection

TL;DR

This paper introduces a context-aware trajectory anomaly detection method that incorporates agent and geographic information to improve detection accuracy, outperforming existing approaches in urban mobility scenarios.

Contribution

It presents a novel trajectory reconstruction framework that integrates contextual data, enhancing anomaly detection performance over prior methods.

Findings

Significantly outperforms existing methods in experiments

Effectively models contextual information for better detection

Validated in two different city datasets

Abstract

Trajectory anomaly detection is crucial for effective decision-making in urban and human mobility management. Existing methods of trajectory anomaly detection generally focus on training a trajectory generative model and evaluating the likelihood of reconstructing a given trajectory. However, previous work often lacks important contextual information on the trajectory, such as the agent's information (e.g., agent ID) or geographic information (e.g., Points of Interest (POI)), which could provide additional information on accurately capturing anomalous behaviors. To fill this gap, we propose a context-aware anomaly detection approach that models contextual information related to trajectories. The proposed method is based on a trajectory reconstruction framework guided by contextual factors such as agent ID and contextual POI embedding. The injection of contextual information aims to improve the performance of anomaly detection. We conducted experiments in two cities and demonstrated that the proposed approach significantly outperformed existing methods by effectively modeling contextual information. Overall, this paper paves a new direction for advancing trajectory anomaly detection.