  The objective of this study is to propose and test an adaptive reinforcement
learning model that can learn the patterns of human mobility in a normal
context and simulate the mobility during perturbations caused by crises, such
as flooding, wildfire, and hurricanes. Understanding and predicting human
mobility patterns, such as destination and trajectory selection, can inform
emerging congestion and road closures raised by disruptions in emergencies.
Data related to human movement trajectories are scarce, especially in the
context of emergencies, which places a limitation on applications of existing
urban mobility models learned from empirical data. Models with the capability
of learning the mobility patterns from data generated in normal situations and
which can adapt to emergency situations are needed to inform emergency response
and urban resilience assessments. To address this gap, this study creates and
tests an adaptive reinforcement learning model that can predict the
destinations of movements, estimate the trajectory for each origin and
destination pair, and examine the impact of perturbations on humans' decisions
related to destinations and movement trajectories. The application of the
proposed model is shown in the context of Houston and the flooding scenario
caused by Hurricane Harvey in August 2017. The results show that the model can
achieve more than 76\% precision and recall. The results also show that the
model could predict traffic patterns and congestion resulting from to urban
flooding. The outcomes of the analysis demonstrate the capabilities of the
model for analyzing urban mobility during crises, which can inform the public
and decision-makers about the response strategies and resilience planning to
reduce the impacts of crises on urban mobility.
