Multigranular Spatio-Temporal Exploration: An Application to On-Street Parking Data

Traffic congestions cost billions of dollars to the society every year and are often aggravated by road users looking for parking. One way of alleviating the parking problem is providing decision makers of smart cities with powerful exploratory tools to analyse the data and find more effective solutions. This paper proposes a novel visual analytics tool for decision makers that allows multigranular spatio-temporal on-street parking data exploration. Even if the tool has been designed to deal with on-street parking data, it relies on a generic logic that makes it adaptable to more general spatio-temporal datasets.