In the United States, most emissions modeling is done using the MOBILE suite of programs. One of the inputs required is vehicle kilometers of travel (VKT) for each vehicle class as a function of average speed. This information traditionally has been obtained from macroscopic planning models whereby the average speed on a given length of roadway is the same for all vehicle classes. During the past 10 years, however, there has been a definite shift away from macro-level transportation modeling to a more simulation-based approach. In addition to the advances in modeling techniques, the advent of intelligent transportation systems (ITS) has opened numerous possibilities for capturing detailed transportation-related information. The purpose of this paper is to illustrate the trade-offs involved under different aggregation scenarios for estimating vehicular emissions using data sets produced by microsimulation models and ITS. A 22-km section of Interstate 10 in Houston, Texas, was used as the test bed in this study. The VKT and speed data for this corridor were determined using a calibrated TRANSIMS model. This information was then used with the MOBILE5a emission model to compute emission estimates. The vehicular emissions were determined for a broad range of spatial, temporal, and combined spatial and temporal levels of disaggregation. It was found that emission estimates could vary by as much as 20 percent, depending on how the VKT and speed data are determined or depending on the type of ITS data-collection technology used.