Increased congestion during peak morning and afternoon periods in urban areas is increasing logistics costs. In addition, environmental, social, and political pressures to limit the impacts associated with CO2 emissions are mounting rapidly. A key challenge for transportation agencies and businesses is to improve the efficiency of urban freight and commercial vehicle movements while ensuring environmental quality, livable communities, and economic growth. However, research and policy efforts to analyze and quantify the impacts of congestion and freight public policies on carriers operations and CO2 emissions are hindered by the complexities of vehicle routing problems with time-dependent travel times and the lack of network-wide congestion data. This research focuses on the analysis of costs and CO2 emissions for different levels of congestion and time-definitive customer demands. Travel time data from an extensive archive of freeway sensors, time-dependent vehicle routing algorithms, and problems-instances with different types of binding constraints are used to analyze the impacts of congestion on commercial vehicle emissions.