The question of whether driver behavior, and speeding in particular, differs based on passenger(s) presence requires the use of large amounts of data, some of which may be difficult to accurately obtain. Traditional methods of obtaining driver behavior information result in datasets that either lack passenger information altogether (i.e., insurance companies using telematics) or rely on rough estimates of passenger age and gender obtained from blurred photos (i.e., naturalistic driving studies like the Second Strategic Highway Research Program). This research project represents a novel, data-driven approach to assessing passenger impact on speeding. Household travel survey demographic information and GPS traces were linked to HERE network speed limit to study the impact of vehicle occupancy on speeding. Survey responses from 11 study areas were cleaned, merged, and ultimately used in developing binomial logistic regression models. Of particular interest were the following driver groups: teenagers, adults driving with child passenger(s), and older drivers. The models suggest that drivers speed less when there is a passenger in the vehicle, particularly adult drivers with a child passenger(s).