High-quality, reliable, and robust data is key to better understanding performance and improvement needs for transportation infrastructure. Predominantly, transportation systems performance has been evaluated using infrastructure-based data, which is often limited by high costs, small sample size, and potential inaccuracy. With recent advancements in technology, previously unobtainable large high-fidelity data, such as probe data and connected vehicle (CV) data, can now be utilized to address many challenges related to transportation systems. This dissertation investigates various research and practical oriented applications for such emerging transportation data sources. The first part of this dissertation develops a novel methodology for characterizing mobility of transportation networks. Using probe vehicle travel times, a route-based travel time reliability metric is proposed for assessing and comparing transportation system’s performance from one geographic area to another. The second part of this dissertation uses CV-technology to develop methodology for improving operational efficiency at a signalized intersection. Two innovative traffic signal control algorithms are established to demonstrate real-time delay optimization for both connected and non-connected vehicles. The third part of this dissertation extends the use of CV-technology to facilitate prioritized freight movement in a signalized corridor. An estimated time of arrival (ETA)-based priority logic is developed, and the proposed priority system is deployed along US-82 in Northport and Tuscaloosa, Alabama. Finally, this dissertation explores the application of emerging transportation data collection technologies to characterize and evaluate transportation systems performance. The techniques presented in this dissertation will be helpful to transportation agencies, planners, and practitioners to assess existing performance and need for future transportation infrastructure.