Where to Park?

Abstract

Several smart cities around the world have begun monitoring parking areas in order to estimate free spots and help drivers that are looking for parking. The current results are indeed promising, however, this approach is limited by the high costs of sensors that need to be installed throughout the city in order to achieve an accurate estimation rate. This work investigates the extension of estimating parking information from areas equipped with sensors to areas that are missing them. To this end, similarity values between city neighborhoods are computed based on background data, i.e., from geographic information systems. Using the derived similarity values, we analyze the adaptation of occupancy rates from monitored- to unmonitored parking areas.