Assuring quality and waiting time in real-time spatial crowdsourcing

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.

Automated summary

In this paper, a time-prediction-based task assignment approach in spatial crowdsourcing (TP-TASC) is proposed to improve task assignment in spatial crowdsourcing. The proposed method predicts travel time using historical data, and assigns tasks to appropriate workers using a heuristic algorithm. Simulation experiments demonstrate that TP-TASC effectively minimizes waiting time and maximizes result quality.