Work

Automatic Traffic Anomaly Detection on the Road Network with Spatial‐Temporal Graph Neural Network Representation Learning

Year: 2022

Type: article

Abstract: Traffic anomaly detection is an essential part of an intelligent transportation system. Automatic traffic anomaly detection can provide sufficient decision‐support information for road network operators, travelers, and other stakeholders. This research proposes a novel automatic traffic anomaly detection method based on spatial‐temporal graph neural network representation learning. We divide traffic anomaly detection into two steps: first is learning the implicit graph feature representation of multivariate time series of traffic flows based on a graph attention model to predict the traffic states. Second, traffic anomalies are detected using graph deviation score calculation to compare the deviation of predicted traffic states with the observed traffic states. Experiments on real network datasets show that with an end‐to‐end workflow and spatial‐temporal representation of traffic states, this method can detect traffic anomalies accurately and automatically and achieves better performance over baselines.

Source: Wireless Communications and Mobile Computing

DOI: 10.1155/2022/4222827

Authors: Hengyuan Zhang, Suyao Zhao, Ruiheng Liu, Wenlong Wang, Yixin Hong, Runjiu Hu

Institution: Zhengzhou University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,550