Exploration on Spatiotemporal Data Repairing of Parking Lots Based on Recurrent GANs

Abstract

It's getting more difficult to find available parking spaces with the rapid growth of the vehicles, especially in big cities. The parking guidance system (PGS) which powered by real time data or historical data could reduce the time spent on looking for parking spaces and alleviate the heavy traffic around the parking lots. However, the PGS is completely ineffective when the parking lots missing data or even have no parking data. This paper makes an exploration on data repairing by digging geospatial data and historical data. First, this paper proposes a method to verify the possibility of parking data similarity when parking lots have spatial similarity. Then take the known data of parking lots as samples and generate reparative data through the Recurrent GANs. The experiment shows, when parking lots have similar spatial features, the parking data, in a high probability, have some similarity with each other, too. And the data generated by Recurrent GANs have the same distribution with real data. In fact, this paper provides a new idea to solve the problems of timeseries data repairing, to a certain extent, this method can help to save the cost of equipment and time.