A Distributed Geospatial Simulation Framework for Massive Spatial Agent-Based Modeling

被引：0

作者：

Zeng M. ^{[1
]}

Hua Y. ^{[1
]}

Zhang Z. ^{[1
]}

Zhang J. ^{[1
]}

Yang Z. ^{[1
]}

Wei Y. ^{[1
]}

机构：

[1] Institute of Surveying and Mapping, Information Engineering University, Zhengzhou

来源：

Journal of Geo-Information Science | 2022年 / 24卷 / 05期

关键词：

Agent-based modeling; Behavior modeling; Distributed computing; Geospatial simulation; Parallel simulation; Real-time visualization; Spatial agent; Virtual geographic environment;

D O I：

10.12082/dqxxkx.2022.220001

中图分类号：

学科分类号：

摘要：

Geospatial simulation based on agent-based modeling is an effective method to recognize and understand dynamic geographic phenomena. As the scale and complexity of geospatial simulation continues to increase, the challenges in model computation increase. Distributed parallel simulation could be used to solve the complex simulation issue of large-scale agents. However, the existing research on building parallel simulation system based on agent modeling/simulation software is not suitable for modeling of spatial agent with high-mobility and behavioral interaction with others, and real-time visualization of simulation process. To solve this problem, this paper proposes a distributed geospatial simulation framework, namely DGSimF, for massive dynamic spatial agent modeling, which supports real-time representation and analysis of the simulation process. A simple but efficient spatial modeling agent for spatial-temporal data is designed, which supports the modeling of integrated geoscience models directly based on agent behavior, adopts the time differentiation method to coordinate the execution of the behavior of each computing node, supports distributed computation in the way of "task parallel" to improve the simulation performance, and constructs a Virtual Geographic Environment (VGE) based on three-dimensional earth rendering engine to support real-time visualization of intermediate simulation results. Finally, the experiments based on the "Red vs. Blue" case are carried out, and the simulation performance with different computation cost and different number of clients is analyzed. The results show that DGSimF can provide an effective platform for massive spatial agent simulation of spatio-temporal feature change and behavior interaction. By expanding the computing nodes, the pressure of complex simulation calculation can be effectively alleviated. Meanwhile, the simulation performance of the proposed framework is high, and the parallel efficiency remains above 0.7 in these experiments. © 2022, Science Press. All right reserved.

引用

页码：815 / 826

页数：11

共 28 条

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