The examination of post-disaster recovery (PDR) in a socio-physical system enables us to elucidate the complex relationships between humans and infrastructures. Although existing studies have identified many patterns in the PDR process, they fall short of describing how individual recoveries contribute to the overall recovery of the system. To enhance the understanding of individual return behavior and the recovery of point-of-interests (POIs), we propose an agent-based model (ABM), called PostDisasterSim. We apply the model to analyze the recovery of five counties in Texas following Hurricane Harvey in 2017. Specifically, we construct a three-layer network comprising the human layer, the social infrastructure layer, and the physical infrastructure layer, using mobile phone location data and POI data. Based on prior studies and a household survey, we develop the ABM to simulate how evacuated individuals return to their homes, and social and physical infrastructures recover. By implementing the ABM, we unveil the heterogeneity in recovery dynamics in terms of agent types, housing types, household income levels, and geographical locations. Moreover, simulation results across nine scenarios quantitatively demonstrate the positive effects of social and physical infrastructure improvement plans. This study can assist disaster scientists in uncovering nuanced recovery patterns and policymakers in translating policies like resource allocation into practice.

翻译：社会物理系统中灾后恢复（PDR）的研究有助于阐明人类与基础设施之间的复杂关系。尽管现有研究已识别出灾后恢复过程的诸多模式，但未能描述个体恢复行为如何影响系统整体恢复。为深入理解个体返回行为及兴趣点（POIs）的恢复机制，我们提出名为PostDisasterSim的智能体模型（ABM）。该模型用于分析2017年哈维飓风后德克萨斯州五个县的恢复过程。具体而言，我们利用手机定位数据和兴趣点数据构建包含人类层、社会基础设施层与物理基础设施层的三层网络。基于已有研究和入户调查，我们开发了该智能体模型，模拟撤离个体返回家园、社会及物理基础设施的恢复过程。通过实施该模型，我们揭示了不同智能体类型、住房类型、家庭收入水平及地理位置在恢复动态中的异质性。此外，九个情景的仿真结果定量证明社会与物理基础设施改善计划的积极效应。本研究可帮助灾害科学家揭示细微恢复规律，并协助政策制定者将资源配置等政策转化为实践。