  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.
