Skip to content. | Skip to navigation

Personal tools

Navigation

You are here: Home / News / Winter weather, transportation infrastructure, and individual decision-making

Winter weather, transportation infrastructure, and individual decision-making

Torrens, Frias-Martinez, and Ghanem use big data from social media to model how people adapt to snowstorms and other disruptions

MPRC Faculty Associates Paul Torrens and Vanessa Frias-Martinez and co-investigator Roger Ghanem (University of Southern California) are using agent-based modeling and big data from social media to study how snowy and icy conditions affect human decisions about transportation, and how these decisions ripple through other infrastructure systems.

Conventional transportation models have traditionally relied on coarse data about land use, socioeconomics and demographics. Then these data are used to estimate the number of trips, trip origins and destinations and choice of transportation mode and route. But in real life, transportation is affected by moment-to-moment decisions by people, who may adjust their transportation routines depending on their individual circumstances and activities. During winter weather, for example, they may telework, leave earlier, leave later or change how they get to their destination. Their decisions are affected not only by their transportation options, but by their employers' policies, school schedules, Internet access and the channels by which they acquire information, as well as weather forecasts.

Torrens, Frias-Martinez, and Ghanem use a technique called agent-based modeling, which allows the individuals or "agents" in a simulation to act independently and interact with one another. "By carefully analyzing sets of big data, we can build realistic motifs of neighborhoods and behaviors, while also protecting the privacy of the individual pieces of data that contributed to those motifs," Frias-Martinez said. "Moreover, we can do this in near-real time for whole cities, essentially filling in the gaps that traditional data sources leave behind."

Ultimately, the tools from this work could help city and regional planners better prepare for and accommodate snowfall and other perturbations. They also could help government agencies better communicate with people who may be impacted.

Read the NSF press release

Read a related story on ScientifcComputing.com

Read a related story on phys.org