Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient trans-portation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. We perform extensive experimental validation using mobility data collected from the City of Nashville, Tennessee, and prove that the method can detect incidents within each cluster in real-time.
J. Islam et al., "Anomaly based Incident Detection in Large Scale Smart Transportation Systems," Proceedings - 13th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2022, pp. 215 - 224, Institute of Electrical and Electronics Engineers, Jan 2022.
The definitive version is available at https://doi.org/10.1109/ICCPS54341.2022.00026
Keywords and Phrases
Anomaly Detection; Cluster Analysis; Graph Algorithms; Incident Detection; Regression; Smart Transportation; Unsupervised Learning
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Jan 2022
National Science Foundation, Grant CNS-1818901