ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life

TL;DR

ReeFRAME is a scalable framework using Reeb graphs to analyze large-scale GPS trajectory data, capturing population and individual patterns for anomaly detection with high efficiency.

Contribution

It introduces a novel Reeb graph-based approach for multi-scale trajectory analysis, enabling scalable and real-time pattern detection in massive datasets.

Findings

Successfully analyzed datasets with up to 500,000 agents

Achieved linear complexity in processing large trajectory data

Effectively detected anomalies in real-time scenarios

Abstract

In this paper, we present ReeFRAME, a scalable Reeb graph-based framework designed to analyze vast volumes of GPS-enabled human trajectory data generated at 1Hz frequency. ReeFRAME models Patterns-of-life (PoL) at both the population and individual levels, utilizing Multi-Agent Reeb Graphs (MARGs) for population-level patterns and Temporal Reeb Graphs (TERGs) for individual trajectories. The framework's linear algorithmic complexity relative to the number of time points ensures scalability for anomaly detection. We validate ReeFRAME on six large-scale anomaly detection datasets, simulating real-time patterns with up to 500,000 agents over two months.