ATD: A Statistical Geo-Enabled Dynamic Human Network Analysis Grant uri icon


  • Recently, new tracking and sensor technologies such as the Global Positioning System (GPS) have been deployed on mobile objects to collect their tracking position with high spatio-temporal resolution. The availability of these massive amounts of tracking data brings great opportunities to many application fields that rely on human movement knowledge. However, the size and the complex spatial temporal dynamic nature of the data also impose challenges for statistical modeling and computation. There is a pressing need to develop computationally efficient quantitative models to handle massive spatial temporal human trajectory data. This project combines theoretical methods and computational approaches to develop novel statistical models, along with efficient algorithms, to meet the increasing demand of efficient analytical tools for massive human trajectory data. The project has broad impact on multiple interdisciplinary fields. The results can be applied to a wide range of practical and important problems including military and national security operations, urban planning, transportation management, traffic forecasting, public health, and social behavioral studies. The increasing use of GPS and other location-aware devices has led to an increasing amount of available human trajectory data at high spatial temporal resolution. Analysis of such data provides invaluable information for many important research problems in different fields. This project will focus on the following research thrusts. First, a new class of trajectory models at the individual level will be developed to describe individual movement behavior in both space and time. With the use of this method, trajectory data is denoised and compressed by a segmented representation with different homogeneous movement states within each segment. In addition, a spatio-temporal point process model is developed to recognize important and complex movement patterns from the segmented trajectories. Extensions beyond the individual trajectory model are then pursued to develop a new class of population level trajectory models that involve a latent dynamic network to describe interactions among individual movements in space and time. Both individual and population trajectory models are carefully designed to allow scalable parallel and online inference algorithms for near real time efficient computations. Finally, the developed methods are used to solve a problem in urban planning with human movement data collected from GPS.

date/time interval

  • 2017 - 2020