Detection and Classification of Sensor Anomalies for Simulating Urban Traffic Scenarios

Sensor network infrastructures are widely used in smart cities to monitor and analyze urban traffic flow. Starting from punctual information coming from traffic sensor data, traffic simulation tools are used to create the digital twin” mobility data model that helps local authorities to better understand urban mobility. However, sensors can be faulty and errors in sensor data can be propagated to the traffic simulations, leading to erroneous analysis of the traffic scenarios. Providing real-time anomaly detection for time series data streams is highly valuable since it enables to automatically recognize and discard or repair sensor faults in time-sensitive processes. In this paper, we implement a data cleaning process that detects and classifies traffic anomalies distinguishing between sensor faults and unusual traffic conditions, and removes sensor faults from the input of the traffic simulation model, improving its performance. Experiments conducted on a real scenario for 30 days have demonstrated that anomaly detection coupled with anomaly classification boosts the performance of the traffic model in emulating real urban traffic.