Modern transportation systems often are meticulously laid out, but a snowy day can dramatically change the way people use them, causing slow downs and other disruptions.
Paul Torrens, a geographer at the University of Maryland, is using big data culled from social media to develop better models of the ways people behave when snow begins to fall. He and his fellow researchers take location- and time-stamped posts from services such as Twitter and use them to create realistic behaviors for virtual humans, or "agents," in what is known as agent-based modeling. The technique creates simulations in which individual agents act independently as well as interacting with other agents.
Using the social media data, Torrens and his team are able to build more realistic behaviors for the agents in their simulations to see how snowfall changes the way people use transportation systems.
One of Torrens' colleagues, Vanessa Frias-Martinez, says the team can now simulate whole cities in near-real time using this technique, which Torrens has used to study earthquake evacuation patterns.
The researchers say their models could prove invaluable to transportation planners and help them build more resilient systems that can absorb the shock of a major snowfall.
From National Science Foundation
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