Given the aging of the population, technologies, including decision-support systems, are needed that are sensitive both to the transportation needs and behaviors of aging drivers and the reliability of the available transportation network in areas more heavily populated by aging adults. Such decision support systems would play a vital role in ensuring increased efficiency, reliability, and connectivity of the nation's highway transportation system, which is currently evolving in response to population aging. These challenges include not only daily traffic operations but also hazard relief response. Central to meeting these challenges are new aging-focused methodologies that will provide agencies with complete, practical, and efficient transportation management and operations procedures. The first step in obtaining such novel methodologies is to extensively evaluate two datasets: existing travel time/traffic data and aging driving patterns. The second step is to integrate them, in order to generate a comprehensive reliability-based model that jointly considers aging adults' travel behavior and traffic on roadway networks. The proposed project will derive this mathematical model through the creation of travel time and network capacity reliability measures based on this integrated database. In order to increase the utility of the model, it will be tested using real-world data from Florida, the state with the highest aging population. Analyses also will examine, using scenarios built in GIS-based transportation network models, selected regions of Florida with particular traffic patterns and age profiles