Random-forest-based task pricing model and task-accomplished model for crowdsourced emergency information acquisition
Wenxiang Li, Sheng-Qun Chen, Lin Lijin, Chen Li · Systems and Soft Computing · 2025
Over the past decade, crowdsourcing has emerged as a powerful tool in various scenarios and has led to an increasing need for crowdsourced emergency management. An important aspect of emergency management includes the acquisition of crowdsourced emergency information. Therefore, to study the emergency information acquisition, we propose a crowdsourced framework. During crowdsourcing, the public is recruited to work on collection of emergency information, such as photos and videos. Therefore, a considerable challenge in crowdsourced emergency information acquisition is to efficiently attract the public to engage in this work. A task price is a significant potential factor that influences public participation. Therefore, a random forest algorithm-based task pricing model and task-accomplished model are computed based on the task attributes and neighboring-workers attributes. In addition,the making money by taking photos dataset is used for a simulation of the proposed method in scikit-learn. Our simulation results demonstrate that the proposed method has an average reduction in Mean Squared Error (MSE) by 44.16 % for task pricing and an average increase in accuracy of 17.71 % for task-accomplished prediction compared to traditional regression models. It is shown that the proposed method has high accuracy and efficiency in crowdsourced emergency information acquisition. Moreover, the proposed method can provide valuable references in the future for emergency information acquisition strategy studies.