Transit services are currently facing several challenges in the United States and around the world. For many reasons, among which the fluctuations in gas prices and the state of the economy are the major ones, transit demand has noticed a considerable increase. The challenge that transit agencies are facing is to make these increases permanent by maintaining transit’s competitive edge over the private vehicle with more dense and reliable service. Current methodologies for scheduling new as well as improving existing transit routes should be able to respond to the dynamic nature of urban traffic as it is evolving through ITS and more comprehensive traffic management strategies. In this research paper, we correlate travel time obtained from buses to travel time obtained from floating vehicles in the Twin Cities metropolitan region. This research helps to introduce more reliable estimates of travel time for planning new and competitive transit services. Specifically, this work studied two bus routes over a variety of different roadway types and traffic conditions and produced statistical models that can estimate travel time based on measurements collected from buses and regular vehicle probes. The generated models revealed the characteristics causing bus service to be generally slower. Altering bus route characteristics can reduce overall travel time and minimize the travel time disparity between buses and private vehicles. In particular, the models presented in this paper lend support to bus-only shoulder policies, stop consolidation, serving major streets with fewer stop signs, and implementation of smart transit signal priority.