Efficient Parking Search using Shared Fleet Data

Finding an available on-street parking spot is a relevant problem of day-to-day life. In recent years, several cities began providing real-time parking occupancy data. Finding a free parking spot in such a smart environment can be modeled and solved as a Markov decision process (MDP). The solver has to consider uncertainty as available parking spots might not remain available until arrival due to other vehicles claiming spots in the meantime. Knowing the parking intention of every vehicle in the environment would eliminate this uncertainty but is currently not realistic. In contrast, acquiring data from a subset of vehicles appears feasible and could at least reduce uncertainty.In this paper, we examine how sharing data within a vehicle fleet might lower parking search times. We use this data to better estimate the availability of parking spots at arrival. Since optimal solutions for large scenarios are computationally infeasible, we base our methods on approximations shown to perform well in single-agent settings. Our evaluation features a simulation of a part of Melbourne and indicates that fleet data can significantly reduce the time spent searching for a free parking bay.