This project has accomplished three significant tasks. First, a state-of-the-art literature review has provided an organizational framework for categorizing the various data fusion projects that have been conducted to date. A popular typology was discussed which situates data fusion technologies in one of three levels, depending on the degree to which sensor data is correlated to provide users with meaningful transit recommendations. The trade-offs that accompany higher-level data fusion efforts--in terms of computing power and memory requirements--were noted. The advantages of multiple-sensor data fusion projects in terms of cost, accuracy, and reliability were also discussed and contrasts were drawn with the traditional deployment of highly accurate, single sensors. Specific techniques of data fusion were described and their possible applications to ITS projects were explored. In fact, this report is one of the first to consider how data fusion technology might be productively applied to the needs of transportation management. A second major component of this report is the description provided of a local data fusion application. This project employs data fusion techniques to correlate input from multiple highway sensors and generate reliable traffic predictions. The resulting information can be displayed for use by commuters as they choose from among various transit options. The architecture of this data fusion system is described in detail. The third component of the project was to create a statistically based algorithm to estimate speed from volume and occupancy measurements. The algorithm presented explicitly accounts for the statistics of the problem and provides a robustness test for the speed estimate.