# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 5 Discussion
### Environmental Impacts: Emissions and Fuel Consumption  
The results demonstrated that platooning AVs can significantly reduce emissions (CO, NOx, VOC) and fuel consumption, particularly at shorter cycle times. For example, platooning AVs showed a reduction in CO emissions by up to 22.9% at 60 s and decreased fuel consumption by up to 22.85% at the same cycle time. This aligns with reductions in NOx and VOC emissions, which further underscore the efficiency of platooning AVs in optimizing traffic flow and reducing environmental impacts.  
While the reduction in emissions and fuel consumption is notable, it is essential to assess whether these environmental benefits justify the cost of implementing platooning technology. Studies show that over time, the savings from reduced fuel consumption and lower emissions, especially in high-traffic urban areas, can offset the initial investment in AV technology [39]. Furthermore, as the technology matures and adoption rates increase, the cost of implementation is expected to decrease, making it a more viable option for sustainable urban transportation systems.  
However, aggressive AVs consistently showed the best performance across all cycle times, achieving the lowest emissions and fuel consumption compared to human-driven vehicles and other AV behaviors. For instance, aggressive AVs at 100% penetration demonstrated a reduction in CO emissions by 23.3% at 60 s, and fuel consumption decreased by up to 23.31% at the same cycle time. These results highlight the potential of aggressive driving algorithms to maximize efficiency and minimize environmental impacts effectively. Although a 100% AV scenario may seem distant, evaluating it provides crucial insights into the maximum potential of AVs to reduce emissions and fuel consumption. These benchmarks serve as a guide for optimizing future AV driving algorithms and traffic systems. As the vehicle fleet evolves potentially incorporating more electric or hybrid AVs this methodology can adapt, making the findings valuable for long-term urban transportation planning. This study helps cities prepare for increasing AV penetration and develop strategies to maximize environmental and operational efficiencies.  
However, the results also revealed that cautious AVs, despite their smoother driving patterns, can paradoxically increase emissions and fuel consumption in scenarios with longer cycle times. This occurs because their conservative driving behaviors lead to underutilization of road capacity, resulting in more idling and slower speeds, as noted in the work of [18; 40]. For example, cautious AVs at 100% penetration increased CO emissions by 220.6% at 60 s and fuel consumption by 220.53% at the same cycle time. This finding highlights a critical challenge for AV deployment: ensuring that overly cautious driving algorithms do not negate the environmental benefits of AV integration, particularly in high-traffic urban environments.  
These insights provide a comprehensive view of the impact of different AV behaviors on environmental and operational efficiencies, underlining the importance of balanced algorithm development to harness the full potential of autonomous vehicle technologies.