Snow and icy conditions affect human decisions about transportation. These decisions can ripple through other infrastructure systems, causing widespread disruptions. Shown here are points of connectivity.

Credit: Paul M. Torrens and Cheng Fu, University of Maryland, College Park; Sabya Mishra, University of Memphis; Timothy Welch, Georgia Tech.

Computational models of the interdependency between the built environment and urban movement under crisis scenarios, such as earthquakes or snow storms, inform real-life situations.

Credit: Paul M. Torrens, Geography and UMIACS, University of Maryland, College Park

A computational human-like "agent" walks through a simulated urban area, acquiring information as it moves and using that information to reason about its actions, reactions and interactions. Agent-based models allow the individuals or agents in a simulation to act independently and interact with one another. Paul Torrens, a researcher at the University of Maryland, uses this technique to better account for the individuality of people. His goal is to understand and predict how disruptions, whether snowfall or earthquakes, can change people's behavior and in turn impact infrastructure.

Credit: Paul M. Torrens, University of Maryland, College Park

Conventional transportation models have traditionally relied on coarse data about land use, socioeconomics and demographics (typically pulled from periodic surveys, such as the decadal census). Then these data are used to estimate the number of trips, trip origins and destinations, and choice of transportation mode and route. University of Maryland researchers use a Web-based geographic information system to monitor interactive connectivity among transit systems in the Washington, D.C., and Baltimore metro areas.

Credit: Paul M. Torrens and Cheng Fu, University of Maryland, College Park; Sabya Mishra, University of Memphis; Timothy Welch, Georgia Tech.

Together, agents represent synthetic populations and communities as they move and interact across urban infrastructure, and can provide insights into the behaviors of different groups.

Credit: Paul M. Torrens, Geography and UMIACS, University of Maryland, College Park

Computational agents walk (or run, depending on the scenario) through a synthetic urban area, acquiring information as they move, using that information to reason about actions, reactions and interactions.

Credit: Paul M. Torrens, University of Maryland, College Park