With the rapid development of mobile devices, spatial crowdsourcing has become an important way to collect data. Task assignment is an important aspect of spatial crowdsourcing. How to improve the quality of the results and decrease the travel distance has been extensively studied in recent years. Existing studies often assume that moving speed is constant or real-time road network information is known. In this paper, the travel time is predicted based on historical data. A framework for time-prediction-based task assignment approach in spatial crowdsourcing (TP-TASC) is proposed. Firstly, a prediction model based on the light gradient boosting machine (LightGBM) is used to predict the travel time of workers with the consideration of the spatial features, the temporal features, and the climate features. Secondly, a heuristic algorithm is proposed to assign the spatial crowdsourcing tasks to appropriate workers. When a task is assigned to a worker, the payment of the worker is also determined automatically. Finally, the simulation experiments based on a real-world taxi-hailing dataset show that the proposed method can not only effectively minimize the task requesters’ waiting time, but also maximize the results’ quality.

随着移动设备的快速发展，空间众包已成为收集数据的重要方式。任务分配是空间众包的一个重要方面。近年来，如何提高结果的质量和减少行程距离已被广泛研究。现有研究通常假设移动速度是恒定的或实时路网信息是已知的。在本文中，旅行时间是根据历史数据预测的。提出了一种空间众包中基于时间预测的任务分配方法（TP-TASC）框架。首先，利用基于光梯度提升机（LightGBM）的预测模型，综合考虑空间特征、时间特征和气候特征，预测工人的出行时间。第二，提出了一种启发式算法将空间众包任务分配给适当的工人。当任务分配给工人时，工人的报酬也会自动确定。最后，基于真实世界打车数据集的仿真实验表明，该方法不仅可以有效地减少任务请求者的等待时间，而且可以最大限度地提高结果质量。