This project studies the fundamental statistical laws governing the mobility patterns of humans. The prior NSF-funded studies conducted by the PI, which are based on GPS traces of 100 people in five different settings including university campuses, New York City, Disney World, and State Fair, revealed that many important fundamental statistical properties of human mobility, namely heavy-tail flight distributions, self-similar dispersion of visit points, and least-action principle for trip planning. Most of all, they find that peo-ple tend to optimize their trips in a way to minimize their discomfort or cost of trips (e.g., distance). The current project is extending this work by considering the effect of temporal constraints. When two persons meet, they have to be at the same location, and also at the same time. A realistic human mobility model must capture these spatial and temporal constraints and dependencies. The major goal of the project is to statistically capture the fundamental laws of these properties which are invariant of specifics in mobility scenarios, and represent them realistically in diverse mobility scenarios including user-created virtual en-vironments. The results from this project include (1) understandings of the fundamental human mobility characteristics and (2) statistical representation of them in synthetic mobility traces. Realistic human mobility models can improve our current practice of evaluating the performance of mobile networks. The models can also be used for other disciplines such as civil engineering for city planning and escape planning, critical disease control for studying the patterns of virus spread, and biology and sociology.
