A Spatial Crowdsourcing Task Assignment Approach Based on Spatio-Temporal Location Prediction

被引：0

作者：

Xu T. ^{[1
]}

Qiao S. ^{[1
]}

Wu J. ^{[2
]}

Han N. ^{[3
]}

Yue K. ^{[4
]}

Yi Y. ^{[5
]}

Huang F. ^{[6
]}

Yuan C. ^{[7
]}

机构：

[1] School of Software Engineering, Chengdu University of Information Technology, Chengdu

[2] School of Securities and Futures, Southwestern University of Finance and Economics, Chengdu

[3] School of Management, Chengdu University of Information Technology, Chengdu

[4] School of Information Science and Engineering, Yunnan University, Kunming

[5] School of Software, Jiangxi Normal University, Nanchang

[6] School of Computer and Information Engineering, Nanning Normal University, Nanning

[7] Guangxi College of Education, Nanning

来源：

Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2022年 / 59卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Location prediction; Minimum cost; Online task assignment; Spatial crowdsourcing; Spatial data intelligence;

D O I：

10.7544/issn1000-1239.20210875

中图分类号：

学科分类号：

摘要：

Space crowdsourcing technology has a varying type of application scenarios in the real physical world, which has been widely concerned by academia and industry. Task assignment is one of the important research issues in space crowdsourcing, that is, distributing workers to appropriate tasks. However, most existing task assignment methods assume that the location and time of crowdsourcing workers and space tasks are known, ignoring the dynamic change of crowdsourcing workers and space tasks in real crowdsourcing platforms. Due to the requirement of strong timeliness in space crowdsourcing platforms, in this situation, the designed assignment method can only get the local optimal results. A new problem of task assignment with maximum value and the minimum cost is proposed, which aims to distribute current and future workers and obtains the maximum assignment value by using the minimum traveling cost. To handle this problem, a trajectory based model is proposed to predict the distribution of tasks. In addition, a kernel density estimation based model is proposed to predict the distribution of workers. A task allocation algorithm based on location prediction is designed to calculate the relative optimal assignment strategy between crowdsourcing workers and spatial tasks. The proposed location prediction method uses graph convolution neural network and ConvLSTM model to predict, which is more accurate and stable than the traditional grid-based location distribution prediction approaches. The heuristic assignment algorithm based on location prediction can complete task allocation in a linear time by combining the predicted location information, which is consistent with the strong timeliness of the space crowdsourcing platform. Extensive experiments are conducted on real datasets to prove the effectiveness of the proposed methods. Compared with the grid-based prediction method, the accuracy of task/worker location prediction is increased by 15.7% and 18.8%, respectively. © 2022, Science Press. All right reserved.

引用

页码：310 / 328

页数：18

共 27 条

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