# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 2 Literature Review
Similarly, a study examined the effects of AVs on driver behavior and traffic performance using VISSIM, finding improvements in average density, travel speed, and travel time during peak hours [15]. Research has focused on the impact of dedicated lanes for CAVs and AVs, demonstrating that higher flow rates and overall traffic throughput are achievable with increased AV penetration rates [16]. Another study explored how AVs technology can enhance the capacity of freeway weaving sections, showing potential capacity increases of up to 15% with higher AV penetration [17]. Recent studies have also delved into the broader implications of AV integration. One literature review conducted on the potential effects of AVs on road transportation emphasized improvements in traffic flow, pedestrian mobility, travel demand, safety, and emissions reduction, while also noting uncertainties regarding long-term impacts [18]. Another study explored the role of CAVs and AVs in enhancing transportation systems' efficiency and sustainability, using PTV Vissim simulation and other tools to provide recommendations for urban planners and policymakers [19]. [PERSON] et al. [20] analyzed the impact of CAV clustering strategies on mixed traffic flow, comparing local coordination with ad hoc strategies. While this study focused on vehicle coordination strategies, our research addresses how different AV behaviors, including platoon-forming, impact performance at intersections, which presents a unique intersection of traffic signal optimization and driving behavior. [PERSON] et al. [21] simulated how AVs influence road capacity in urban networks, finding that AVs increase road network capacity by 16-23% at full penetration. However, our research builds upon this by investigating the role of AV penetration rates in mixed-traffic  
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**Reference** & **Year** & **The Study Objective** & **Methodology/Tools Used** & **Key Findings** \\ \hline
[22] & 2021 & Review of car-following models for human and autonomous driving behaviors & Literature review of car-following models; comparison of traditional cars with human drivers to AVs; discussion on AV-ready tools in micro-simulation in micro-simulation platforms & ThProvides an overview of various car-following models for both human-driven and AVs. Highlights the importance of AV-ready tools in micro-simulation platforms for accurate modeling of vehicle dynamics and environments. & Thared AVs can significantly reduce parking space demand, allowing real-location for other uses such as bike-sharing spots, bike lanes, additional traffic lanes, or parklets. environments and their impact on intersection-specific metrics like delays, emissions, and fuel consumption. [PERSON] et al. [22] provided a review of car-following models for AVs and human-driven vehicles, highlighting the importance of AV-ready tools in simulation platforms. Our study similarly leverages advanced traffic simulation tools (PTV VISSIM) but applies them to specific intersection performance metrics, offering practical insights into AV behavior's effect on urban traffic. [PERSON] et al. [23] examined the impact of shared AVs on urban parking and space management, showing that shared AVs can reduce parking space demand. Although this study is centered on urban space management, our research is focused on traffic performance and how different AV driving logics affect key metrics at intersections. Finally, [PERSON] et al. [24] and [PERSON] [25] explored AV driving logics and their impacts on traffic performance. [PERSON] et al. [24] analyzed various scenarios using PTV VISSIM, highlighting the importance of assessing AV-readiness in infrastructure. Similarly, [PERSON] [25] focused on traffic performance at a four-leg intersection in Sweden, finding that cautious AVs negatively impact performance.