$\mathrm{W}^{2}$Parking: A Data-Driven Win-Win Contract Parking Sharing Mechanism Under Both Supply and Demand Uncertainties

Abstract

With the rapid growth of the number of private vehicles, searching for accessible parking spaces becomes intractable for drivers, especially during high-demand hours. In recent years, we are witnessing a number of sharing economy services. Contract parking sharing, as an innovative sharing economy mode, has the potential to alleviate the difficult parking issue and make full use of the urban parking resources. However, the uncertainties of both drivers' parking demand and owners' sharing supply make it challenging to achieve efficient sharing. Thanks to IoT technology, many current parking lots now record vehicles' fine-grained parking data for billing purposes. Leveraging these fine-grained parking data, we exploit available contract parking spaces to share them with drivers that have temporary parking demand. Specifically, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{W^{2}}$</tex-math></inline-formula> Parking, a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</u> in- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</u> in contract <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parking</u> sharing system, which includes two key components: (i) an idle time prediction model to estimate available periods of parking spaces and (ii) a parking sharing model to schedule temporary users to have access to these available parking spaces under both demand and supply uncertainties using dynamic programming combined with a 2-approximation algorithm with performance-bound guarantees. we evaluate our system on seven-month real-world parking data from 368 parking lots with 14,704 parking spaces. Extensive experimental results show that our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{W^{2}}$</tex-math></inline-formula> Parking achieves more than 90% of accuracy in parking time prediction, and the utilization rate of contract parking spaces is improved by 35%