Beschreibung:
The geographic location of crowdsourcing participants greatly affects the quality of spatial crowdsourcing task assignments. Most existing studies used grid models to describe locations, resulting in a drop in accuracy and ignoring the correlations between tasks in urban road networks. In reality, crowdsourcing tasks tend to appear on the side of roads rather than randomly generated. So, the location of tasks can be described in terms of its relationship to roads. Thus, a new method of describing the geographic location of tasks and workers is proposed in this paper. This method considers the road network to alleviate the low accuracy of distance estimation. Besides, it can reflect the correlations between two positions in the road network. Aiming at task allocation in the road network, two algorithms are proposed. One considers the movement of workers and the other considers parallel tasks. By experimental comparison, the feasibility and effectiveness of the proposed algorithms are validated. Simulation experiment analysis found the number of workers and tasks on the same map scale affect the ratio of accepted tasks and the average moving distance, and a higher number of workers and tasks benefits the ratio of accepted tasks and reduce the average moving distance