Low-cost service to predict and manage indoor parking spaces

Abstract

Since the number of vehicles on the road has been growing rapidly during the last few years, the lack of parking spaces has become a usual problem for many people. Taking advantage of the emerging concept of connected car, the popularity of smartphones and the rise of Internet of Things, this work proposes a solution to predict where the best available parking spots are. The proposal includes both a centralized system to predict empty indoor parking spaces based on cellular automata, and a low-cost mobile application based on different technologies to help the driver to find empty parking spaces. On the one hand, cellular automata are used to model the behavior of drivers in parking facilities. Specifically, the system applies the idea behind the game of life to capture some features of parking occupancy based on common user behaviors, in order to reduce the time to find empty parking spots. On the other hand, the proposal involves a smartphone application that uses accurate technologies for indoor positioning. The client software is a lightweight Android application that provides different indoor positioning solutions, such as precise positioning systems based on Quick Response codes or Near Field Communication tags, or semi-precise positioning systems based on Bluetooth Low Energy beacons. The proposed service takes into account that it will be gradually adopted by users. The results obtained from a preliminary implementation show how the proposal improves the parking experience and increases efficiency of parking facilities in terms of time and energy costs.