SParking: a win-win data-driven contract parking sharing system

With a rapid growth of vehicles in modern cities, searching for a parking space becomes difficult for drivers especially in rush hours. To alleviate parking difficulties and make the most of urban parking resources, contract parking sharing services allow drivers to pay for parking under the consent of owners, reaching a win-win situation. Contract parking sharing services, however, have not yet been prevailingly adopted due to the dynamic parking time which leads to uncertainties for sharing. Thanks to the Internet of things technique, most of modern parking lots record vehicles' fine-grained parking data including entry and exit timestamps for billing purposes. Leveraging the parking data, we analyze and exploit available vacant contract parking spaces. We propose SParking, a shared contract parking system with a win-win data-driven scheduling. SParking consists of (i) a parking time prediction model to exploit reliable periods of free parking spaces and (ii) an optimal scheduling model to allocate free parking spaces to drivers. To verify the effectiveness of SParking, we evaluate our design on seven-month real-world parking data involved with 368 parking lots and 14,704 parking spaces in Wuhan, China. The experimental results show that SParking achieves more than 90% of accuracy in parking time prediction and the average utilization rate of contract parking spaces is improved by 35%.