Evaluating Effectiveness and Identifying Appropriate Methods for Anomaly Detection in Intelligent Transportation Systems

Anomaly detection is a crucial application of traffic management to intelligent transportation systems (ITSs). An ITS utilizes predictive models to identify potential problems and improves system operation reliability by analyzing real-time data streams from millions of sensors, actuators, and other recording devices. However, the collected data typically contain anomalies that can lead to inaccurate traffic system estimates. To evaluate the effectiveness of anomaly detection algorithms on ITS data, comparative analyses of different algorithms are conducted using both labeled and unlabeled data collected from the California Department of Transportation Performance Measurement System. The results highlight the robustness of certain algorithms, such as CBLOF (Cluster-Based Local Outlier Factor), in detecting both point and collective anomalies. Additionally, this study introduces an enhanced dynamic CBLOF algorithm that integrates adaptive windowing and incremental learning, enabling real-time updates and enhanced responsiveness to evolve traffic conditions. Experimental findings demonstrate that this enhancement significantly boosts anomaly detection accuracy while maintaining computational efficiency, making it well-suited for dynamic traffic scenarios. Furthermore, we explored the fusion of traffic domain knowledge with data features in anomaly detection. Our findings emphasize the importance of selecting appropriate algorithms that consider specific problems, evaluation indicators, and data characteristics. This research provides valuable insights for transportation authorities seeking to improve their ability to detect anomalous traffic events, ultimately leading to more effective traffic management and congestion reduction strategies.