Recognition and recommendation of parking places

Abstract

Current solutions to recommend available parking spaces rely on options like: intentional user feedback; installing data collectors in volunteering fleet vehicles, or; installing static sensors to monitor available parking spaces. In this paper we propose a solution based application that runs on commodity smartphones and makes use of the advanced sensor capabilities in these devices, along with methods of statistical analysis of the collected sensor data to provide useful recommendations. We exploit a combination of k-medoid clustering and Conditional Random Fields to reliably detect a user parking with a limited sensor capability. Next, we outline a method based on Markov Chains to calculate the probability of finding a parking space near a given location. We also enhance the solution with more sensor capability to discover desirable properties in parking spaces.