The goal of this proposal is to develop novel modeling techniques to infer individual activity patterns from the large scale cell phone datasets and taxi data from NYC. As such this research offers a paradigm shift from traditional transportation modeling by using large scale, disaggregate data and provides an unique perspective to understand the complex interactions among human behavior, urban environments and traffic patterns. The proposed research will develop a model of dynamic activity pattern using the geo-location data to understand where, when and how long people participate in activities in a given day. The research will contribute to the understanding of activity schedule development and traffic prediction on a traffic network. In addition, it has the potential to contribute to traffic forecasting, predictive traveler guidance, urban event planning and management and disaster preparedness.