Anomaly Detection in Road Networks Using Sliding-Window Tensor Factorization

TL;DR

This paper introduces a tensor-based model utilizing sliding-window tensor factorization to detect multiple types of anomalies in road networks, improving traffic management and emergency response capabilities.

Contribution

It presents a novel, efficient tensor factorization algorithm for multi-type anomaly detection in road networks, including path-level anomaly inference.

Findings

Effective detection of multiple anomaly types demonstrated

Outperforms baseline methods in synthetic and real-world tests

Path-level anomaly identification enhances traffic analysis

Abstract

Anomaly detection in road networks is vital for traffic management and emergency response. However, existing approaches do not directly address multiple anomaly types. We propose a tensor-based spatio-temporal model for detecting multiple types of anomalies in road networks. First, we represent network traffic data as a 3rd-order tensor. Next, we acquire spatial and multi-scale temporal patterns of traffic variations via a novel, computationally efficient tensor factorization algorithm: sliding window tensor factorization. Then, from the factorization results, we can identify different anomaly types by measuring deviations from different spatial and temporal patterns. Finally, we discover path-level anomalies by formulating anomalous path inference as a linear program that solves for the best matched paths of anomalous links. We evaluate the proposed methods via both synthetic experiments and case studies based on a real-world vehicle trajectory dataset, demonstrating advantages of our approach over baselines.