Traffic sensor health monitoring using spatiotemporal graphical modeling

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Published Nov 19, 2020
Linjiang Wu Chao Liu Tingting Huang Anuj Sharma Soumik Sarkar

Abstract

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.

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Keywords

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References
Adenis, P.,Wen, Y., & Ray, A. (2012). An inner product space on irreducible and synchronizable probabilistic finite state automata. Mathematics of Control, Signals, and Systems, 23(4), 281–310.
Bengea, S., Li, P., Sarkar, S., Vichik, S., Adetola, V., Kang, K., . . . Kelman, A. (2015). Fault-tolerant optimal control of a building heating, ventilation and air conditioning system. Science and Technology for the Built Environment, 21, 734-751.
Bhuiyan, M. Z. A., Wang, G., & Wu, J. (2009). Target tracking with monitor and backup sensors in wireless sensor networks. In Computer communications and networks, 2009. icccn 2009. proceedings of 18th internatonal conference on (pp. 1–6).
Chen, C., Petty, K., Skabardonis, A., Varaiya, P., & Jia, Z. (2001). Freeway performance measurement system: mining loop detector data. Transportation Research Record: Journal of the Transportation Research Board(1748), 96–102.
Dailey, D. J. (1999). A statistical algorithm for estimating speed from single loop volume and occupancy measurements. Transportation Research Part B: Methodological, 33(5), 313–322.
Federal size regulations for commercial motor vehicles. (2016). U.S. Department of Transportation Federal Highway Administration.
Harris, T., Gamlyn, L., Smith, P., MacIntyre, J., Brason, A., Palmer, R., . . . Slater, A. (1995). ’neural-maine’: intelligent on-line multiple sensor diagnostics for steam turbines in power generation. In Neural networks, 1995. proceedings., ieee international conference on (Vol. 2, pp. 686–691).
Hinton, G. E. (2012). A practical guide to training restricted boltzmann machines. In Neural networks: Tricks of the trade (pp. 599–619). Springer.
Jeong, H., Kim, H., Lee, S., & Dornfeld, D. (2006). Multisensor monitoring system in chemical mechanical planarization (cmp) for correlations with process issues. CIRP Annals-Manufacturing Technology, 55(1), 325–328.
Jiang, Z., & Sarkar, S. (2015). Understanding wind turbine turbine interactions using spatiotemporal pattern network. In Proceedings of asme dynamics systems and control conference.
Klein, L. A., Mills, M. K., & Gibson, D. R. (2006). Traffic detector handbook: -volume ii (Tech. Rep.).
Krishnamurthy, S., Sarkar, S., & Tewari, A. (October 2014). Scalable anomaly detection and isolation in cyberphysical systems using bayesian networks. In Proceedings of asme dynamical systems and control conference, san antonio, tx, usa.
Liu, C., Ghosal, S., Jiang, Z., & Sarkar, S. (2016). An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed cps. In Cyber-physical systems (iccps), 2016 acm/ieee 7th international conference on (pp. 1–10).
Liu, C., Ghosal, S., Jiang, Z., & Sarkar, S. (2017). An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling. Cyber-Physical Systems. doi: 10.1080/23335777.2017.1386717
Liu, C., Huang, B., Zhao, M., Sarkar, S., Vaidya, U., & Sharma, A. (December 2016). Data driven exploration of traffic network system dynamics using high resolution probe data. In Proceedings of ieee conference on decision and control, las vegas, nv.
Minge, E. D., Peterson, S., Weinblatt, H., Coifman, B., & Hoekman, E. (2012). Loop-and length-based vehicle classification: Federal highway administration-pooled fund program [tpf-5 (192)] (Tech. Rep.). Minnesota Department of Transportation, Research Services.
Mukherjee, K., & Ray, A. (2014). State splitting and merging in probabilistic finite state automata for signal representation and analysis. Signal processing, 104, 105–119.
Najafi, M., Gulp, C., & Langari, R. (2004). Enhanced autoassociative neural networks for sensor diagnostics (eaann). In Fuzzy systems, 2004. proceedings. 2004 ieee international conference on (Vol. 1, pp. 453–456).
Rao, C., Ray, A., Sarkar, S., & Yasar, M. (2009). Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns. Signal, Image and Video Processing, 3(2), 101-114.
Ray, A. (2004). Symbolic dynamic analysis of complex systems for anomaly detection. Signal Processing, 84(7), 1115–1130.
Sallans, B., Bruckner, D., & Russ, G. (2005). Statistical model-based sensor diagnostics for automation systems. IFAC Proceedings Volumes, 38(2), 239–246.
Sarkar, S., Mukherjee, K., Sarkar, S., & Ray, A. (2013). Symbolic dynamic analysis of transient time series for fault detection in gas turbine engines. Journal of Dynamic Systems, Measurement, and Control, 135(1), 014506.
Sarkar, S., Sarkar, S., Mukherjee, K., Ray, A., & Srivastav, A. (2013). Multi-sensor information fusion for fault detection in aircraft gas turbine engines. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 227(12), 1988–2001.
Sarkar, S., Sarkar, S., Virani, N., Ray, A., & Yasar, M. (2014). Sensor fusion for fault detection and classification in distributed physical processes. Frontiers in Robotics and AI, 1, 16.
Sarkar, S., & Srivastav, A. (2016). A composite discretization scheme for symbolic identification of complex systems. Signal Processing, 125, 156–170.
Sarkar, S., Srivastav, A., & Shashanka, M. (2013). Maximally bijective discretization for data-driven modeling of complex systems. In American control conference (acc), 2013 (pp. 2674–2679).
Solo, V. (2008). On causality and mutual information. In Decision and control, 2008. cdc 2008. 47th ieee conference on (pp. 4939–4944).
Wang, R., Zhang, L., Sun, R., Gong, J., & Cui, L. (2011). Easitia: A pervasive traffic information acquisition system based on wireless sensor networks. IEEE Transactions on Intelligent Transportation Systems, 12(2), 615–621. Wavetronix smartsensor hd user guide. (2016). Wavetronix.
Wells, T. J., Smaglik, E. J., & Bullock, D. M. (2008). Health monitoring procedures for freeway traffic sensors, volume 1: Research report. Joint Transportation Research Program, 318.
Wenjie, C., Lifeng, C., Zhanglong, C., & Shiliang, T. (2005). A realtime dynamic traffic control system based on wireless sensor network. In Parallel processing, 2005. icpp 2005 workshops. international conference workshops on (pp. 258–264).
Wibral, M., Rahm, B., Rieder, M., Lindner, M., Vicente, R., & Kaiser, J. (2011). Transfer entropy in magnetoencephalographic data: Quantifying information flow in cortical and cerebellar networks. Progress in biophysics and molecular biology, 105(1), 80–97.
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Technical Papers