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It is predicted that half of the vehicles sold and 40% of vehicle travels could be autonomous in the 2040s (Litman 2017). However, how the presence of connected and autonomous vehicles (CAV) impacts highway capacity and network system performance remains unclear. Without this knowledge, it is hard to understand and quantify the implication of the disruptive CAV technologies on the existing traffic operations. Also, it would be difficult for relevant agencies (e.g., MPOs and state DOTs) to make appropriate long-term planning for preparing the infrastructure systems for emerging mixed CAV traffic. This project aims to explore methods for tackling these challenges and demonstrating their applicability via real-world case studies. First, an analytical approach for quantifying highway capacity in mixed traffic environments is developed. This approach considers CAV technology uncertainties considering different headway distributions and vehicle platooning configurations. A generalized capacity function for mixed CAV traffic for the full spectra of traffic density, CAV penetration rates, vehicle types, platooning configurations and highway segment types is explored. The proposed model has a simple and practical form for easy applications by relative stakeholders. Then, we demonstrate applications of the proposed mixed traffic analysis approach to a transportation system with a case study on the Tampa Bay Regional Planning Model (http://www.tbrta.com/). Further, we propose simple traffic flow fundamental diagram formulations for ACC equipped vehicles from both macroscopic and microscopic perspectives. Then, a new methodological framework …