Batch-Based Cooperative Task Assignment in Spatial Crowdsourcing

The rapid development of the spatial crowdsourcing platform in the fields of express delivery, food delivery, and intelligent transportation has attracted widespread attention. As a typical problem in spatial crowdsourcing, online task matching problem has been widely studied. Most of the existing researches are based on the task allocation of different optimizations under one single platform. Recently, in order to solve the situation of non-uniform distribution of tasks and crowd workers on a single platform, cross online task assignment has been proposed aiming at increasing the mutual benefit through cooperations. However, existing methods lead to the situation where the local platform lends workers to other platforms, resulting in a lack of workers of itself. In this paper, we propose a Batch-Based Cooperative Task Assignment(BCTA) problem, which enables multi-platform task assignment to be completed within a tolerant time. We design a BCTA model and propose fixed-t BCTA(FT-BCTA) algorithm and adaptive BCTA(Adt-BCTA) algorithm to solve the BCTA problem. FT-BCTA focuses on a fixed batching strategy, while Adt-BCTA considers the batching strategy adaptively according to the supply and demand of multi-platforms. Extensive experiments on both real datasets and synthetic datasets show the effectiveness and efficiency of our algorithms.