Abstract
Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system’s efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.
Highlights
Agent-based modeling (ABM) is an important and efficient approach to understand dynamic geospatial phenomena in a bottom-up manner [1,2,3,4]
In addition to efficient parallelization support, online visualization is a feature of the 4D-SAS when compared with existing parallel simulation platforms, e.g., waterlog route avoidance in the Case 1 and fire front propagation in the Case 2 are displayed to users/analysts in time without much delay
This paper presents a powerful distributed simulation system for massive geospatial process modeling, 4D-SAS, facilitated by high-performance computing
Summary
Agent-based modeling (ABM) is an important and efficient approach to understand dynamic geospatial phenomena in a bottom-up manner [1,2,3,4]. The requirements for efficient realistic visualization of parallel ABM simulations raise important unsolved issues To address these challenges, this paper introduces a distributed dynamic-data driven simulation and analysis system (4D-SAS) for massive spatial agent-based modeling on high performance computing platforms. These two models were both fed with dynamic data, e.g., traffic jams or wind speeds, to update the simulation environment status in real-time Experimental results from these two simulations demonstrate that 4D-SAS is an efficient platform and suitable for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata
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