A KAIST research team headed by Prof. Song Chong of the School of Electrical Engineering and Computer Science has developed a new statistical model that simulates human mobility patterns, mimicking the way people move over the course of a day, a month or longer, university sources said on Tuesday (May 12).
The model, developed in collaboration with scientists at North Carolina State University, is the first to represent the regular movement patterns of humans using statistical data.
The model has a variety of potential uses, ranging from land use planning to public health studies on epidemic disease.
The researchers gave global positioning system (GPS) devices to approximately 100 volunteers at five locations in the U.S. and South Korea and tracked the participants" movements over time. By tracing the points where the study participants stopped, and their movement trajectories, researchers were able to determine patterns of mobility behavior.
The researchers were then able to emulate these fundamental statistical properties of human mobility into a model that could be used to represent the regular daily movement of humans. The model, called Self-similar Least Action Walk (SLAW), will have a wide array of practical applications.
The research, "SLAW: A Mobility Model for Human Walks," was presented on April 20 at the 28th IEEE Conference on Computer Communications in Rio de Janeiro, Brazil. The National Science Foundation of the U.S. funded the research.