Utility-Distribution Aware Real-Time Cross Online Matching in Spatial Crowdsourcing

Spatial crowdsourcing platforms have become indispensable in addressing the evolving needs of modern society. These platforms facilitate essential services such as ride-sharing, on-demand food delivery, and efficient parcel distribution. However, the uneven distribution of workers and requests under a single-platform setting may lead to the loss of tasks. To address this issue, we introduce the Cross Online Matching (COM) problem, which facilitates collaboration among multiple platforms. We first propose DemCOM and RamCOM, which adopt deterministic greedy and randomized trade-off strategies, respectively. Furthermore, we develop a Utility-Distribution Aware Cooperative Online Matching (UDACOM) algorithm that leverages supply-demand relationships to optimize decision-making. Theoretical analysis confirms the competitive ratios of our algorithms. Validated on both real and synthetic datasets, our approach significantly outperforms state-of-the-art methods, achieving a 5% increase in total revenue and a 3% improvement in the successful matching rate.