User Location Prediction in Mobile Crowdsourcing Services

In recent years, mobile crowdsourcing has been integrated into people's lives. A variety of mobile crowdsourcing services have emerged and been widely used, such as Gigwalk, Foursquare, and Uber. Due to the uncertainty of task distribution and workers' trajectory, as well as diverse worker interests and capabilities, it is crucial to effectively predict the mobile workers' trajectories such that they are willing to get to the location and perform their tasks with as little travel and time cost as possible. In this paper, we propose a context-sensitive prediction approach for workers' moving path in mobile crowdsourcing services. We predict the upcoming location of workers through movement rules, real-time perception of workers' moving path and contexts when assigning spatial tasks on a crowdsourcing platform, thereby pushing a task to the workers who will enter the region within the deadline of the task. Our location prediction method can avoid workers' extra cost such as time and charges in performing tasks. The analysis and simulation experiments based on real data sets show that this method can effectively predict the location of a worker and achieve better results in task assignment and completion.