Aiming at online task allocation problem of spatiotemporal crowdsourcing,a task range adjustment algorithm DMRA and an online task allocation algorithm PAMA based on predictive analysis are proposed.The DMRA algorithm takes task location as the center and dynamically adjusts the range of tasks according to worker density.The PAMA algorithm uses Bayesian classifier to predict the distribution of the next timestamp object based on historical statistical probability.On this basis,the weighted bipartite graph optimal matching algorithm is executed to complete the task allocation.Experimental results show that the combination of DMRA algorithm and PAMA algorithm can improve the total utility of task allocation and reduce the travel cost of workers,and the performance of task allocation is better than that of greedy algorithm and random threshold algorithm.
spatiotemporal crowdsourcing;online task allocation,
total utility of allocation,
travel cost of workers,
Bayesian classification prediction,