Pricing Research on Spatial Crowdsourcing Tasks Under Incompletely Uncertain Scene Information
Lin Weida, Dong Hongbin · 2023
Task pricing is an important step for crowd-sourcing platforms to solve profit-driven task allocation and maximize profits. Most of the existing researches only carry out algorithm design on the premise of fully determining the scene information. However, due to the interference of many factors in the real scene, information such as workers and task costs in the scene is usually not completely uncertain. To solve the above problems, a spatial crowdsourcing task pricing algorithm is proposed. Firstly, the algorithm uses the proposed dimension-up gray wolf algorithm and support vector regression(DU_GWO-SVR) to predict the task price, and then sets the price based on the obtained price. In order to solve the instability of average price and matching number caused by dynamic supply and demand, an adjustment mechanism is designed to stabilize the average price of tasks. The experiment uses a real data set-the New York taxi data set, and compares it with the classic greedy algorithm and binary matching algorithm. The experimental results show that the matching rate of the proposed algorithm is above 75% when the scene information is not completely uncertain.