TY - GEN
T1 - Data-driven detection of anomalies and cascading failures in traffic networks
AU - Basak, Sanchita
AU - Ayman, Afiya
AU - Laszka, Aron
AU - Dubey, Abhishek
AU - Leao, Bruno
N1 - Funding Information:
This research is funded in part by a grant from Siemens, CT and the following grants from National Science Foundation: 1818901 and 1647015.
Publisher Copyright:
© 2019 Prognostics and Health Management Society. All rights reserved.
PY - 2019/9/23
Y1 - 2019/9/23
N2 - Traffic networks are one of the most critical infrastructures for any community. The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical community infrastructure. Our focus in this paper is on the failure dynamics of traffic networks. By failure, we mean in this domain the hindrance of the normal operation of a traffic network due to cyber anomalies or physical incidents that cause cascaded congestion throughout the network. We are specifically interested in analyzing the cascade effects of traffic congestion caused by physical incidents, focusing on developing mechanisms to isolate and identify the source of a congestion. To analyze failure propagation, it is crucial to develop (a) monitors that can identify an anomaly and (b) a model to capture the dynamics of anomaly propagation. In this paper, we use real traffic data from Nashville, TN to demonstrate a novel anomaly detector and a Timed Failure Propagation Graph based diagnostics mechanism. Our novelty lies in the ability to capture the the spatial information and the interconnections of the traffic network as well as the use of recurrent neural network architectures to learn and predict the operation of a graph edge as a function of its immediate peers, including both incoming and outgoing branches. Our results show that our LSTM-based traffic-speed predictors attain an average mean squared error of 6:55 × 10-4 on predicting normalized traffic speed, while Gaussian Process Regression based predictors attain a much higher average mean squared error of 1:78 × 10-2. We are also able in an AUC (Area Under Curve) of 0.8507 for the precisionrecall curve. To study physical traffic incidents, we augment the real data with simulated data generated using SUMO, a traffic simulator. Finally, we analyzed the cascading effect of the congestion propagation by formulating the problem as a Timed Failure Propagation Graph, which led us in identifying the source of a failure/congestion accurately.
AB - Traffic networks are one of the most critical infrastructures for any community. The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical community infrastructure. Our focus in this paper is on the failure dynamics of traffic networks. By failure, we mean in this domain the hindrance of the normal operation of a traffic network due to cyber anomalies or physical incidents that cause cascaded congestion throughout the network. We are specifically interested in analyzing the cascade effects of traffic congestion caused by physical incidents, focusing on developing mechanisms to isolate and identify the source of a congestion. To analyze failure propagation, it is crucial to develop (a) monitors that can identify an anomaly and (b) a model to capture the dynamics of anomaly propagation. In this paper, we use real traffic data from Nashville, TN to demonstrate a novel anomaly detector and a Timed Failure Propagation Graph based diagnostics mechanism. Our novelty lies in the ability to capture the the spatial information and the interconnections of the traffic network as well as the use of recurrent neural network architectures to learn and predict the operation of a graph edge as a function of its immediate peers, including both incoming and outgoing branches. Our results show that our LSTM-based traffic-speed predictors attain an average mean squared error of 6:55 × 10-4 on predicting normalized traffic speed, while Gaussian Process Regression based predictors attain a much higher average mean squared error of 1:78 × 10-2. We are also able in an AUC (Area Under Curve) of 0.8507 for the precisionrecall curve. To study physical traffic incidents, we augment the real data with simulated data generated using SUMO, a traffic simulator. Finally, we analyzed the cascading effect of the congestion propagation by formulating the problem as a Timed Failure Propagation Graph, which led us in identifying the source of a failure/congestion accurately.
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U2 - 10.36001/phmconf.2019.v11i1.861
DO - 10.36001/phmconf.2019.v11i1.861
M3 - Conference contribution
AN - SCOPUS:85081301116
T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
A2 - Clements, N. Scott
A2 - Zhang, Bin
A2 - Saxena, Abhinav
PB - Prognostics and Health Management Society
T2 - 11th Annual Conference of the Prognostics and Health Management Society, PHM 2019
Y2 - 23 September 2019 through 26 September 2019
ER -