<ul><li>The study focuses on employing clustering techniques to analyze traffic flow data and detect anomalies, including sensor failures and irregular congestion events.</li><li>Multiple clustering approaches are explored, including partitioning and hierarchical methods, paired with various time-series representations and similarity measures.</li><li>Hierarchical clustering with symbolic representations is found to provide robust segmentation of traffic patterns, while partitioning methods like k-means and fuzzy c-means yield meaningful results when paired with Dynamic Time Warping.</li><li>The proposed anomaly detection strategy successfully identifies sensor malfunctions and abnormal traffic conditions with minimal false positives, making it useful for real-time monitoring.</li></ul>

Detection of Anomalous Vehicular Traffic and Sensor Failures Using Data Clustering Techniques

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