# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 2 Literature Review  
The integration of AVs into urban traffic systems has garnered considerable attention, with numerous studies exploring their potential impacts on traffic performance and efficiency. Existing study has predominantly focused on understanding how AVs affect traffic flow, safety, and congestion at signalized intersections and other critical points in urban infrastructure (Table 1). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **The Study Objective** & **Methodology/Tools Used** & **Key Findings** \\ \hline
[14] & 2016 & Investigate the impact of CAVs and AVs on traffic flow stability and throughput; optimize AHS performance & Microscopic traffic flow simulation model; developed control schemes and simulation frameworks & CAVs and AVs enhance traffic flow stability and throughput, optimize AHS capacity, reduce congestion, and minimize emissions. & AVs improve average density by 8.09\%, travel speed by 8.48\%, and travel time by 9.00\% during peak hours. AVs reduce congestion and improve safety. CVs near AVs adopt shorter THW. AVs reduce situation awareness and may increase drowwsiness. & CAV dedicated lanes achieved higher flow rates. Overall traffic flow throughput increased with higher CAV penetration rates. The optimal strategy is one CAV-dedicated lane above 40\% penetration, and two lanes above 60\%. Individual CAV performance is crucial for lane effectiveness. & \\ \hline
[17] & 2018 & Investigate how AVs technology can enhance operations and increase capacity of weaving sections & Developed a multiclass hybrid model; calibrated and validated using empirical data from a weaving section; applied in a simulation-based optimization framework & Higher penetration of AVs increases wearing section capacity. \\ \hline
[18] & 2019 & Investigate the potential effects of AVs on road transportation & Literature review of existing studies on AV impacts & Non-linear capacity increase observed. Optimal lane change distributions can prevent capacity reduction. Potential capacity increase of up to 15\%. \\ \hline
[19] & 2019 & Explore the role of CAVs and AVs in enhancing transportation systems’ efficiency, safety, and sustainability; assess urban infrastructure’s impact on transportation networks and the benefits of integrating CAVs; provide recommendations for policymakers and urban planners & AVs improve traffic flow efficiency, reduce queue delays and travel times. The study highlighted the importance of urban infrastructure in supporting visualization tools; literature review and expert consultations & CAVs improve traffic flow efficiency, reduce queue delays and travel times. The study highlighted the importance of urban infrastructure in supporting CAV integration, providing recommendations for effective transportation planning with CAVs. \\ \hline
[20] & 2020 & Analyze the impact of CAV clustering strategies on mixed traffic flow characteristics & Analysis of vehicle trajectory data; compared ad hoc and local coordination strategies for CACC & Local coordination outperforms ad hoc in network throughput. Improved network performance and safety. Hard braking events for HVs change significantly under local coordination. \\ \hline
[21] & 2020 & Investigate how AVs influence road capacity in urban traffic networks & Simulations using SUMO software; analyzed grid and real-world networks with varying AV penetration levels & AVs increase road network capacity. Maximum traffic flows with 100\% AVs were 16–23\% higher than with only conventional vehicles. Significant capacity improvements were observed around 40–50\% AVs penetration. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of studies investigating the impact of AV transportation systems. For instance, [PERSON] et al. [14] investigated the influence of CAVs and AVs on traffic flow stability and throughput, highlighting the enhanced performance of automated highway systems (AHS) using control schemes and simulation frameworks [14].