Making Sense of ANPR Data via Intelligent Spatio-temporal Disaggregation of Traffic Flows

Michiel Dhont, Elena Tsiporkova, Nicolás González-Deleito, Bruno Cornelis

Abstract: It becomes common practice to equip our cities and streets with a wide range of sensors (e.g., cameras, inductive loops and push buttons for pedestrians). Those sensors are paving the way for smart monitoring of traffic, which is nowadays already used to, for example, dynamically steer traffic flows by controlling traffic lights or track stolen vehicles. However, such data remains very complex to thoroughly inspect and interpret. In this paper we exploit an advanced spatio-temporal disaggregation technique to extract insights from ANPR data. More specifically, we visualise and analyse spatial and temporal insights from vehicle detections along street segments in a dense network of ANPR cameras in the police district of Voorkempen, Belgium. The proposed analysis tools can be integrated in an intelligent transportation system (ITS) to support traffic monitoring by the appropriate authorities.