Campus Units

Civil, Construction and Environmental Engineering, Mechanical Engineering, Institute for Transportation

Document Type


Publication Version

Published Version

Publication Date


Journal or Book Title

International Journal of Prognostics and Health Management





First Page



Accurate traffic sensor data is essential for traffic operation management systems and acquisition of real-time traffic surveillance data depends heavily on the reliability of the traffic sensors (e.g., wide range detector, automatic traffic recorder). Therefore, detecting the health status of the sensors in a traffic sensor network is critical for the departments of transportation as well as other public and private entities, especially in the circumstances where real-time decision is required. With the purpose of efficiently determining the sensor health status and identifying the failed sensor(s) in a timely manner, this paper proposes a graphical modeling approach called spatiotemporal pattern network (STPN). Traffic speed and volume measurement sensors are used in this paper to formulate and analyze the proposed sensor health monitoring system and historical time-series data from a network of traffic sensors on the Interstate 35 (I-35) within the state of Iowa is used for validation. Based on the validation results, we demonstrate that the proposed approach can: (i) extract spatiotemporal dependencies among the different sensors which leads to an efficient graphical representation of the sensor network in the information space, and (ii) distinguish and quantify a sensor issue by leveraging the extracted spatiotemporal relationship of the candidate sensor(s) to the other sensors in the network.

Research Focus Area

Transportation Engineering


This article is published as Wu, Linjiang, Chao Liu, Tingting Huang, Anuj Sharma, and Soumik Sarkar. "Traffic sensor health monitoring using spatiotemporal graphical modeling," International Journal of Prognostic and Health Management 9, no. 1 (2018): 022. Posted with permission.

Creative Commons License

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

Copyright Owner

Linjiang Wu et al.



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