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.

In accordance with the Federal Highway Administration (FHWA) “Interim Guidance Update on Mobile Source Air Toxic Analysis in NEPA Documents (September 30, 2009),” transportation projects subject to the National Environmental Policy Act (NEPA) must include an analysis of mobile source air toxics (MSATs). MSATs are air pollutants emitted by mobile sources that can cause serious health effects. Of a group of 93 MSAT compounds, the U.S.

Speeding is the leading contributing factor in fatal accidents in NY state, according to NY State Department of Motor Vehicle Accidents Statistical Summary (2009). Understanding and modeling speeding and speed control is one of major challenges in human performance modeling which involves: a) the modeling of several aspects of human cognitive system: perception, decision making and motor control as well as their interaction with the vehicle model; b) individual differences in speed control and prediction of speeding in real time.