ViCTS: A novel network partition algorithm for scalable agent-based modeling of mass evacuation
ViCTS: A novel network partition algorithm for scalable agent-based modeling of mass evacuation
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DOI：
10.1016/j.compenvurbsys.2019.101452
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Dandong Yin;Shaowen Wang;Y. Ouyang
中科院分区：
文献类型：
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作者：
Dandong Yin;Shaowen Wang;Y. Ouyang
紧急疏散是应对飓风、洪水和地震等致命灾害的关键措施，但大规模紧急疏散本身是一个复杂的过程，有时可能导致混乱的局面和意想不到的后果。在许多紧急情况下，大规模疏散是必要的，以科普严重的公共威胁在狭窄的时空范围内。为了更好地理解复杂的现象，如大规模疏散，并研究可能的后果，基于代理的模型（ABMs）已被广泛发展在以前的工作。现有的模型模拟个人行为，当应用于大的地理区域和复杂的行为时，会带来计算挑战。解决这种计算挑战的一个关键策略是将交通网络划分为较小的区域，并利用先进的网络基础设施和网络GIS解决相应的计算成本。在这项研究中，开发了一种新的网络分区算法，以提高基于代理的大规模疏散建模的可扩展性的基础上，利用空间运动模式的紧急疏散的前沿cyberGIS支持的计算框架。具体来说，该算法被称为基于目标偏移的Voronoi聚类，或ViCTS。它是受网络Voronoi图的启发，旨在解决疏散交通的独特特性所造成的计算可扩展性的挑战。我们进行了一组计算实验与真实的街道网络数据在不同的疏散场景，以测试算法的有效性和效率。计算实验表明，ViCTS优于一个广泛使用的网络分区算法微观流量模拟方面实现最佳的计算性能，通过平衡计算负载和减少跨高性能并行计算资源的通信。
Emergency evacuation is a critical response to deadly disasters such as hurricanes, floods, and earthquakes, etc. However, mass emergency evacuation itself is a complex process that sometimes could lead to chaotic situations and unintended consequences. In many emergency scenarios, mass evacuation is necessary to cope with severe public threats within tight spatiotemporal ranges. To better understand complex phenomena like mass evacuation, and study possible consequences, agent-based models (ABMs) have been widely developed in previous work. Existing models simulate individual behaviors, posing computational challenges when applied to large geographic areas and sophisticated behaviors. A key strategy for resolving such computational challenges is to partition transportation networks into smaller regions and resolve corresponding computational costs by taking advantage of advanced cyberinfrastructure and cyberGIS. In this study, a novel network partition algorithm is developed to improve the scalability of agent-based modeling of mass evacuation based on a cutting-edge cyberGIS-enabled computational framework that exploits the spatial movement patterns of emergency evacuation. Specifically, the algorithm is termed as Voronoi Clustering based on Target-Shift, or ViCTS. It is enlightened by network Voronoi diagrams and designed to resolve computational scalability challenges caused by the unique characteristics of evacuation traffic. We conducted a set of computational experiments with real street network data in various evacuation scenarios to test the effectiveness and efficiency of the algorithm. Computational experiments show that ViCTS outperforms a widely used network partition algorithm for microscopic traffic simulation in terms of achieving optimal computational performance by balancing computational loads and reducing communications across high-performance parallel computing resources.