Accelerating Benders decomposition approach for shared parking spaces allocation considering parking unpunctuality and no-shows

Abstract

By allocating idle private parking spaces to demanders, shared parking reuses idle resources and effectively alleviates parking problems. In practice, there may be parking unpunctuality behavior of demanders, as well as the potential no-show behavior of owners and demanders. These stochastic factors affect the allocation of shared parking spaces. In this paper, we study the shared parking spaces allocation problem considering parking unpunctuality and no-shows. First, the allocation problem considering parking unpunctuality is formulated as a stochastic programming model, with the objective to maximize the profit of the shared parking platform. Sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm is exploited to solve the model effectively, where initial optimality cuts are explored to speed up the convergence of the algorithm. Then the proposed approach is extended to incorporate no-shows. Numerical experiments show great performance of the proposed approach. The experiments also show that the fluctuations of the unpunctual time and the no-show probability have a significant impact on the shared parking system, and balanced supply and demand is helpful to increase the profit of the platform and the satisfaction of demanders.