Semi Real-time Data Cleaning of Spatially Correlated Data in Traffic Sensor Networks
Federica Rollo, Chiara Bachechi, Laura Po · 2022
The new Internet of Things (IoT) era is submerging smart cities with data.Various types of sensors are widely used to collect massive amounts of data and to feed several systems such as surveillance, environmental monitoring, and disaster management.In these systems, sensors are deployed to make decisions or to predict an event.However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data.By their nature, sensors are prone to errors, therefore identifying and filtering anomalies is extremely important.This paper proposes an anomaly detection and classification methodology for spatially correlated data of traffic sensors that combines different techniques and is able to distinguish between traffic sensor faults and unusual traffic conditions.The reliability of this methodology has been tested on real-world data.The application on two days affected by car accidents reveals that our approach can detect unusual traffic conditions.Moreover, the data cleaning process could enhance traffic management by ameliorating the traffic model performances. INTRODUCTIONPublic Administrations have begun to capture the large amount of data collected through IoT sensors in order to face the big challenge of sustainable development.Nowadays, many cities are equipped with traffic sensors installed on their road networks.The most diffuse sensor type is the induction loop: static sensors that are embedded under the road surface and provide real-time vehicle count and speed estimation.These data can be used as input to simulate realtime traffic scenarios that can effectively help Public Administration to cope with the mobility challenge -and instantaneously optimizing the transportation flow while sending new instructions to smart city devices like traffic lights.Traffic sensors are of great value for urban traffic modeling.However, they are not free of errors and faults, and the degradation of sensor performance can heavily affect the output of traffic model (Bachechi et al., 2020c).Therefore, detecting faulty traffic sensors is a fundamental step in order to boost the quality of the traffic management system (Bachechi et al., 2020d; Bachechi et al.,