# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 6 Conclusions  
This study provides a comprehensive analysis of the impact of different autonomous vehicle (AV) driving behaviors on urban traffic performance at a signalized intersection. The simulation results demonstrate that AV behaviors have varying effects on traffic efficiency, emissions, and fuel consumption. Cautious AVs, despite promoting smoother traffic flow, led to significant increases in queue lengths and delays, particularly at higher penetration rates. For instance, at 100% penetration and a 60 s cycle time, cautious AVs increased queue lengths by up to 82.30% and delays by 59.03%. Additionally, emissions and fuel consumption saw dramatic rises, with CO emissions and fuel consumption increasing by 220.6% and 220.53%, respectively, at shorter cycle times. This indicates that overly conservative driving behaviors can hinder road capacity utilization, leading to negative environmental impacts. In contrast, aggressive AVs showcased significant benefits, particularly at 100% penetration. Aggressive AVs reduced queue lengths by up to 56.62%, travel times by 21.15%, and vehicle delays by 27.59% at a 60 s cycle time. They also decreased CO emissions by 23.3% and fuel consumption by 23.31%. However, at longer cycle times, such as 204 s, the benefitsdiminished due to increased traffic oscillations, reducing queue length savings to 23.14% and travel time improvements to 6.63%. Platooning AVs exhibited strong performance, especially at shorter cycle times, where CO emissions were reduced by up to 22.9% and fuel consumption by 22.85%. Their coordinated driving behavior effectively optimized road usage, minimizing inter-vehicle gaps and enhancing overall traffic flow. Normal AVs provided a balanced performance, reducing queue lengths by up to 33.91% and delays by up to 15.26%, offering an optimal compromise between aggressive and cautious driving behaviors. Mixed AV environments, featuring a blend of AV behaviors, demonstrated stable performance, with reductions in queue lengths ranging from 14.34% to 38.82% and vehicle delays decreasing by 0.85% to 16.98%. This approach mitigated the extremes of individual AV behaviors, resulting in smoother and more predictable traffic flow. These findings underscore the transformative potential of AVs to improve traffic efficiency and environmental sustainability. However, they also highlight the importance of optimizing AV algorithms and traffic signal control strategies to realize the benefits of AV integration in urban traffic systems fully. Balancing aggressive and cautious behaviors while leveraging platooning technology could be key to maximizing the operational and environmental advantages of AVs. For future research, it is recommended to undertake the following:  
* Investigate the long-term impacts of mixed driving scenarios on traffic patterns and urban mobility. * Explore necessary infrastructure modifications to support AV integration. * Study the behavioral nuances of platooning at higher penetration rates. * Focus on designing energy-efficient AV systems to minimize environmental impacts. * Develop comprehensive policy and regulatory frameworks for AV deployment. * Conduct real-world pilot studies to validate simulation results and gather empirical data. * Examine the potential for extrapolating and modeling various performance metrics (queue lengths, travel times, delays, emissions, and fuel consumption) to enhance understanding of AV impacts. The contributions of the authors to the paper are specified as follows: [PERSON] was responsible for data curation, formal analysis, writing the original draft, and validation of the results; [PERSON] provided supervision throughout the research process; S.S.S.M.Q. also contributed to the study's conceptualization and provided supervision. All authors have read and agreed to the published version of the manuscript. The authors declare that this study received no financial support. Not applicable. All the data used in this article are provided throughout the article.