Real-Time Data Cleaning in Traffic Sensor Networks

Through deploying Internet of Things (IoT) technologies, many aspects of the urban environment can be monitored in real-time. Mobility, pollution, parking, waste, lighting can be controlled and managed in an intelligent city thanks to a low-cost sensor network. Such big data streams generated in realtime by sensors need to be handled with appropriate techniques to detect erroneous measurements instantly. In this paper, we implement a fast data cleaning process to remove traffic sensor faults. Then, we present a traffic model that takes advantage of the detection of anomalous data measured by traffic sensors. Experiments on a real case scenario have demonstrated that anomaly detection can further improve the performance of a traffic model in emulating the real urban traffic.