The Introduction of Connected-Automated Vehicle (CAV) technology provided a new opportunity to fix the traditional transportation system. Automated vehicles (AuV) would take the driving responsibility and drive the vehicles by analyzing their' surrounding through a range of sensors. The connectivity feature of these vehicles would facilitate to sense of the roadway and traffic conditions beyond the range of sensors and make informed decisions. While the vehicles equipped with these technologies becoming more common, large-scale market penetration will take a long time. Hence, our transportation infrastructure will pass through a transitional phase where both Human-driven vehicles (HuV) and AuVs share the roadway. Additionally, the prosperity and acceptance of these technologies depend on a clear understanding of the implications of overcoming the limitations of the traditional transportation system. My research focused on developing a comprehensive modeling framework to establish numerical simulation of both types of vehicles (i.e., HuVs, AuVs ) while recognizing the variations of driving behaviors of human drivers. Modeling both vehicle types provided the opportunity to explore diverse mixed traffic scenarios to attain extensive insights into such traffic conditions. Prior to developing the modeling framework, the variations of the human driving patterns were identified through extensive analysis of real-world human driving data. Bi-directional (i.e., longitudinal, lateral) control features were analyzed to comprehend human instincts during driving which can be integrated with the human driver modeling. Further analysis was performed to classify driving behaviors based on these features for the short and long term. The upsides of studying human driving behavior rest not only on better understanding for modeling human drivers but also on designing automated vehicles capable of addressing the variations of human driver behavior. The behavioral classification approach in this part of the research used three vehicular features known as jerk, leading headway, and yaw rate to classify human drivers into two groups (Safe and Hostile Driving) on short-term classification, and drivers' habits are categorized into three classes (Calm Driver, Rational Driver, and Aggressive Driver). Through the proposed method, behavior classification has been successfully identified in 86.31 ± 9.84% of speeding and 87.92 ± 10.04% of acute acceleration instances. Afterward, the foundation of mixed traffic modeling was developed through car-following strategy formulation. This part of the research proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and measured shifts in traffic mobility and safety as a result. Additionally, this part of the research explores the influences of platoon properties (i.e. Intra-platoon Headway, Inter-platoon Headway, Maximum Platoon Length) on traffic stream characteristics. Different combinations of HuVs and AuVs are simulated in order to understand the variations of improvements induced by AuVs in a traffic stream. Simulation results reveal that grouping AuVs at the front of the traffic stream to apply CACC-based car-following model will generate maximum mobility benefits for the traffic. Higher mobility improvements can be attained by forming long, closely spaced AuVs at the cost of reduced safety. To achieve balanced mobility and safety advantages from mixed traffic movements, dynamically optimized platoon configurations should be determined at varying traffic conditions and AuVs market penetrations. Finally, grounded on prior research on human driving behavior and modeling framework of mixed traffic, this research objectively experimented with bi-directional motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. This part of research begins by establishing a multilane microscopic model for studied vehicle types from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the HCM. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share while steadiness in the average speed of traffic was experienced with increasing AuV share. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state as well as the optimal AuV share for the current inflow rate in weaving sections.