Diagnosing urban traffic anomalies by integrating geographic knowledge and tensor theory

Abstract

ABSTRACT Urban traffic anomaly diagnosis is crucial for urban road management and smart city construction. Most existing methods perform anomaly detection from a data-driven perspective and ignore the unique spatiotemporal characteristics of traffic anomalies, resulting in reduced accuracy or incorrect extraction of anomalies. In this study, we integrate geographic methods with high-dimensional tensor theory and develop a comprehensive framework for traffic anomaly diagnosis. The framework is divided into three parts: road traffic status acquisition, traffic anomaly detection, and urban anomaly analysis. First, we perform filtering and map matching on the trajectory data to obtain the traffic states of urban roads and construct them into a tensor. For traffic anomaly detection, a novel Spatio-Temporal constrained Low-Rank Sparse Tensor (ST-LRST) method is constructed to decompose urban traffic data into normal and anomalous components. Specifically, ST-LRST introduces a truncated nuclear norm, as a tighter nonconvex alternative to the tensor low-rank function, to more accurately estimate daily traffic patterns, thus improving the extraction accuracy of the sparse anomaly tensor. The spatiotemporal features of traffic anomalies are embedded into the sparse tensor to reduce the fragmentation and error rate of urban anomaly identification. To analyze anomalies, we study the characteristics of the anomalies detected by ST-LRST, such as duration, impact scope, and impact intensity. Subsequently, combined with crowdsourced geographic information, we comprehensively analyze the anomalous spatiotemporal variations in the road network and explain the factors that induce these urban anomalies. Experiments are performed using traffic data from Xi’an, China, and the results indicate that ST-LRST achieves the most accurate and comprehensive diagnosis of urban anomalies.