# Anomaly Detection in Road Networks Using Sliding-Window Tensor Factorization

arXiv: https://arxiv.org/abs/1803.04534 · HTML: https://mlsanity.com/paper/1803.04534/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams](https://mlsanity.com/paper/2102.11517/) (2021)
- [Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling](https://mlsanity.com/paper/2409.11529/) (2024)
- [Robust Spatiotemporally Contiguous Anomaly Detection Using Tensor Decomposition](https://mlsanity.com/paper/2510.00460/) (2025)

## Abstract (arXiv)

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.