Spatial platforms have become increasingly important in people's daily lives. Task assignment is a critical problem in these platforms that matches real-time orders to suitable workers. Most studies only focus on independent platforms that are in a competitive relationship. Recently, an emerging service model was proposed, where orders are shared with multiple similar platforms. It aims to solve the imbalance between supply and demand through cooperation. However, it faces the following main challenges: 1) Coordinating independent platforms fairly based on the limited information; 2) Building a task assignment process with personalized algorithms. In this paper, we study real applications and define the Autonomy and Coordination Task Assignment problem (ACTA) to maximize the global revenue and fairness. We propose a framework to solve ACTA that consists of public order sending, local matching, global conflict adjustment and results notification. The framework uses mid-products and public data to train a revenue estimation model to coordinate participants. We further propose dynamic weight task assignment algorithms to guarantee fairness. Through the experiments, we prove that the platforms can obtain higher revenue, which shows the effectiveness and efficiency of our work.

Online spatial crowdsourcing platforms provide popular O2O services in people's daily. Users submit real-time tasks through the Internet and require the platform to immediately assign workers to serve them. However, the imbalance distribution of tasks and workers leads to the rejection of some tasks, which reduces the profit of the platform. In this paper, we propose that similar platforms can form an alliance to make full use of the global service supply through cooperation. We name the problem asGlobalTaskAssignment (GTA), in which platforms are allowed to hire idle workers from other platforms to improve the profit of all the platforms together. Different from relevant works, the decision-makers in GTA are platforms rather than individual workers, which can better assign workers in all platforms and improve the overall profit. We design an auction-based incentive mechanism (AIM), to motivate platforms to rent idle workers to other platforms so that increase their own profit. Based on the mechanism, we propose a greedy-based assignment algorithm (BaseGTA), in which platforms greedily maximizes their current profit. We further propose a prediction-based assignment algorithm (ImpGTA), in which platforms make decisions based on the spatial-temporal distribution in the future time. Experimental results show that platforms using our algorithms can achieve higher profit than the existing studies.