Data-Driven Approaches for Smart Parking

Abstract

Finding a parking space is a key problem in urban scenarios, often due to the lack of actual parking availability information for drivers. Modern vehicles, able to identify free parking spaces using standard on-board sensors, have been proven to be effective probes to measure parking availability. Nevertheless, spatio-temporal datasets resulting from probe vehicles pose significant challenges to the machine learning and data mining communities, due to volume, noise, and heterogeneous spatio-temporal coverage. In this paper we summarize some of the approaches we proposed to extract new knowledge from this data, with the final goal to reduce the parking search time. First, we present a spatio-temporal analysis of the suitability of taxi movements for parking crowd-sensing. Second, we describe machine learning approaches to automatically generate maps of parking spots and to predict parking availability. Finally, we discuss some open issues for the ML/KDD community.