Task Assignment Method in Spatial Crowdsourcing Based on Graph Search

Abstract

In the research on the urban crowdsourcing distribution service scheduling, one of the keys is the spatial crowdsourcing task assignment. Aiming at the current spatial crowdsourcing lacking of the consideration for the actual road network, a graph-based crowdsourcing task optimization assignment model and its optimization algorithm are proposed, in order to obtain the maximum number of courier assigned tasks at a time. The optimal assigned task and delivery path of the courier are obtained under the condition of satisfying the goal and constraints. In this study, at first the actual geographical location is mapped into a map structure, considering the crowdsourcing task starting location, task target location and the courier location. Then, three graph-based spatial task assignment methods are implemented: the first is to realize the realistic grab strategy, location service-based grab strategy (LSGS); the second is crowdsourcing task assignment algorithm based on the task location (TATL); the third is crowdsourcing task assignment algorithm based on ant colony planning (TAAP). Finally, through the comparison of multiple sets of experiments, the proposed TATL and TAAP algorithms can obtain the distribution path and improve the task assignment efficiency.