TitleThrough artificial intelligence, UTSA researchers build community resilience

"As urbanization accelerates, urban communities - particularly those predominantly comprised of racial and ethnic minorities - face unique and complex challenges, weakening the resilience of the community," said Zhai. "These include increased poverty rates, displacement, relocation, limited access to quality education, disparities in healthcare access, and substandard housing conditions."

The project aims to alleviate these challenges by using responsible, foundational AI models and tools to create urban digital twins.

A digital twin is a virtual model of an object or system that uses real-time data to simulate, monitor or predict behavior. Digital twins can operate with ongoing, two-way data, making them the most precise way to represent a city and its actual systems.

The project leads hope to create an urban digital twin that will simulate an urban community and analyze its resilience and weak points.

"A digital twin of a city offers planners and citizens a powerful tool for understanding how changes in planning and infrastructure can impact a community's resilience to natural hazards," said Zhai. "By digitally modeling and testing various resilience scenarios, planners, policymakers and citizens can identify the most effective, science-based strategies for hazard resiliency interventions. The platform will gather vast amounts of data, including information on people, vehicles, structures and infrastructure, enabling the simulation of hazard events under different policy or response scenarios to assess their potential impacts."

Zhai added that the project's ultimate goal is to develop innovative solutions to address urban challenges.

"Digital twins can provide the insights needed to inform resilient decision-making in vulnerable communities," he said. "We're examining the LiDAR sensor and low-cost environmental sensor technologies, artificial intelligence, mechanisms and policies needed to increase community resilience."

The team will use the data collected with these tools for 3D modeling of cities and micro-climate simulations.

Modeling multiple facets of a community accurately and in real time can be challenging, as communities involve multiple interconnected elements. Those elements include the people, infrastructure and environments, which influence one another in a complex feedback loop.

"During a severe storm, for example, predicting human behavior is difficult," said Zhai. "Some residents may follow evacuation orders, while others might stay behind to protect their homes, even though it's risky. This unpredictability complicates resource allocation. It's hard to determine the number of emergency shelters needed or how many rescue personnel are dispatched. Additionally, panic or misinformation could lead to unexpected traffic jams or crowding in specific areas, further complicating emergency response efforts. Incorporating these unpredictable human behaviors into a real-time model adds significant complexity."

The team hopes that a digital twin powered with AI will be able to cut through these layers of uncertainty, providing valuable insights that help policy makers anticipate needs and plan for challenges.

"This project can help to better visualize and predict long-term resilience risks such as population growth, housing and transportation demand, and all the various urban issues that can come from such core changes," said López Ochoa. "For example, understanding how, when and why people move to a new home within the city can help both explain and address issues such as job absenteeism, delays, lack of labor supply, food deserts and even housing affordability."

In the process, the team also hopes to integrate AI into new research projects and academic programs at UTSA, engage students in leading-edge research and cultivate a workforce with in-demand skills such as AI, urban planning and data science.

"We are excited to offer UTSA students funded opportunities to work on urban research that is at the knowledge frontier," said López Ochoa.

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