Connected and automated vehicles (CVs and AVs, respectively) are rapidly emerging paradigms aiming to deploy and develop transportation systems that enable automated driving and data exchange among vehicles, infrastructure, and mobile devices to improve mobility, enhance safety, and reduce the adverse environmental impacts of transportation systems. Based on these premises, the focus of this research is to quantify the potential benefits of CVs and AVs to provide insight into how these technologies will impact road users and network performance. To assess the traffic operational performance of CVs, a connectivity-based modeling framework was developed based on traffic microsimulation for a real network in the city of Toronto. Then the effects of real-time routing guidance and advisory warning messages were studied for CVs. In addition, the impact of rerouting of non-connected vehicles (non-CVs) in response to various sources of information, such as mobile apps, GPS or VMS, was considered and evaluated. The results demonstrate the potential of such systems to improve mobility, enhance safety, and reduce greenhouse gas emissions (GHGs) at the network-wide level presented for different CVs market penetration. Additionally, the practical application of CVs in travel time estimation and its relationship with the number and location of roadside equipment (RSE) along freeways was investigated. A methodology was developed for determining the optimal number and location of roadside equipment (RSE) for reducing travel time estimation error in a connected vehicle environment. A simulation testbed that includes CVs was developed and implemented in the microsimulation model for Toronto 400-series highway network. The results reveal that the suggested methodology is capable of optimizing the number and location of RSEs in a connected vehicle environment. The optimization results indicate that the accuracy of travel time estimates is primarily dependent on the location of RSEs and less dependent on the total density of RSEs. In addition to CVs, the potential capacity increase of highways as a function of AVs market penetration was also studied and estimated. AVs are classified into Cooperative and Autonomous AVs. While Autonomous AVs rely only to their detection technology to sense their surroundings, Cooperative AVs, can also benefit from direct communication between vehicles and infrastructure. Cooperative car-following and lane-changing models were developed in a microsimulation model to enable AVs to maintain safe following and merging gaps. This study shows that cooperative AVs can adopt shorter gap than autonomous AVs and consequently, can significantly improve the lane capacity of highways. The achievable capacity increase for autonomous AVs appears highly insensitive to the market penetration, namely, the capacity remains within a narrow range of 2,046 to 2,238 vph irrespective of market penetration. The results of this research provide practitioners and decision-makers with knowledge regarding the potential capacity benefits of AVs with respect to market penetration and fleet conversion. / Thesis / Doctor of Philosophy (PhD)