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New Human Movement Model Can Aid In Studying Epidemic Outbreaks, Public Planning

Researchers have 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. The model, developed by scientists at North Carolina State University and the Korea Advanced Institute of Science and Technology (KAIST), is the first to represent the regular movement patterns of humans using statistical data. The model has a host of potential uses, ranging from land use planning to public health studies of disease outbreak.

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, according to study co-author Dr. Injong Rhee, a professor of computer science at NC State. By plotting the points where the study participants stopped, and their movement trajectories, researchers were able to determine patterns of mobility behavior.

For example, Rhee says, the researchers found that people tend to perform multiple activities in clusters that are in close proximity to each other – such as going to a bank, a dry-cleaner and a pharmacy that are all located on the same street. Furthermore, the researchers found that the study participants tend to more frequently visit locations that are popular among other people.

These behaviors illustrated statistical patterns. For example, Rhee explains, people will try to make the most efficient use of their time and effort by clustering activities together that are in geographical proximity to each other. This behavior creates patterns in which people make many short “jumps” within the clustered areas while making a few long jumps among the clustered areas. These patterns are best explained by statistical processes called self-similar points of visits and power-law distribution of jumping distances.

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, Rhee says. The model is called the Self-similar Least Action Walk (SLAW), which could have a wide array of practical applications.

For example, Rhee says, “a realistic human mobility model could be used by civil engineers to plan roads, by public health officials to study virus outbreak spread, or by telecommunication companies for planning where to locate cell-phone towers. Any situation where you would want to be able to predict where people will go.”

The research, “SLAW: A Mobility Model for Human Walks,” was presented April 20 at the 28th IEEE Conference on Computer Communications in Rio de Janeiro, Brazil.

The research team that developed the model includes Rhee, NC State Ph.D. candidate Seongik Hong, NC State post-doctoral research associate Seong Joon Kim, and KAIST researchers Kyunghan Lee and Song Chong. The National Science Foundation funded the research.


Note to editors: The presentation abstract follows.

“SLAW: A Mobility Model for Human Walks”

Authors: Seongik Hong, Seong Joon Kim and Injong Rhee, North Carolina State University; Kyunghan Lee and Song Chong, Korea Advanced Institute of Science and Technology

Presented: April 20, 2009, at the 28th IEEE Conference on Computer Communications in Rio de Janeiro, Brazil

Abstract: Simulating human mobility is important in mobile networks because many mobile devices are either attached to or controlled by humans and it is very hard to deploy real mobile networks whose size is controllably scalable for performance evaluation. Lately various measurement studies of human walk traces have discovered several significant statistical patterns of human mobility. Namely these include truncated power-law distributions of flights, pause-times and inter-contact times, fractal way-points, and heterogeneously defined areas of individual mobility. Unfortunately, none of the existing mobility models effectively captures all of these features. This paper presents a new mobility model called SLAW (Self-similar Least Action Walk) that can produce synthetic walk traces containing all these features. This is by far the first such model. Our performance study using SLAW generated traces indicates that SLAW is effective in representing social contexts present among people sharing common interests or those in a single community such as university campus, companies and theme parks. The social contexts are typically common gathering places where most people visit during their daily lives such as student unions, dormitory, street malls and restaurants. SLAW expresses the mobility patterns involving these contexts by fractal waypoints and heavy-tail flights on top of them. We verify via DTN (delay-tolerant network) routing performance evaluation using SLAW that these patterns bring out the unique performance features of various mobile network routing protocols.