Campus Units

Civil, Construction and Environmental Engineering, Mechanical Engineering

Document Type


Publication Version

Published Version

Publication Date


Journal or Book Title

International Journal of Prognostics and Health Management





First Page


Last Page



Traffic dynamics in the urban interstate system are critical in terms of highway safety and mobility. This paper proposes a systematic data mining technique to detect traffic system-level anomalies in a batch-processing fashion. Built on the concepts of symbolic dynamics, a spatiotemporal pattern network (STPN) architecture is developed to capture the system characteristics. This novel spatiotemporal graphical modeling approach is shown to be able to extract salient time series features and discover spatial and temporal patterns for a traffic system. An information-theoretic metric is used to quantify the causal relationships between sub-systems. By comparing the structural similarity of the information-theoretic metrics of the STPNs learnt from each day, a day with anomalous system characteristics can be identified. A case study is conducted on an urban interstate in Iowa, USA, with 11 roadside radar sensors collecting 20-second resolution speed and volume data. After applying the proposed methods on one-month data (Feb. 2017), several system-level anomalies are detected. The potential causes that include inclement weather condition and non-recurring congestion are also verified to demonstrate the efficacies of the proposed technique. Compared to the traditional predefined performance measures for the traffic systems, the proposed framework has advantages in capturing spatiotemporal features in a fast and scalable manner.

Research Focus Area

Transportation Engineering


This article is published as Huang, Tingting, Chao Liu, Anuj Sharma, and Soumik Sarkar, "Traffic System Anomaly Detection using Spatiotemporal Pattern Networks," International Journal of Prognostics and Health Management 9, no. 1 (2018).

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Copyright Owner

The Authors



File Format