## 7 Discussion  
In this study, we implemented and compared three methods for traffic signal optimization --particle swarm optimization (PSO), grey wolf optimization (GWO), and hybrid PSO-GWO. Our results show that the PSO-GWO hybrid is generally the best approach; it prompted a significant improvement in efficiency within traffic relative to standard PSO or GWO techniques. The hybrid approach effectively combined the convergence speed of PSO, which excels in reducing traffic wait times, with the powerful searching power of GWO, which adjusts signal times to reduce congestion and improve overall performance. This flexibility made their combination particularly effective in managing dynamic traffic conditions in real time, which is essential in large urban areas.  
While simulation created a controlled environment in which to test the system, its real-world application will be further complicated by factors such as unpredictable driver behavior, variable road infrastructure, weather conditions, and sensor reliability. Moving on to real-world testing is essential if we are to verify the robustness of the PSO-GWO hybrid system.  
Such testing may include deploying the system at a controlled intersection equipped with traffic sensors and cameras to evaluate performance metrics such as reducing waiting times, increasing productivity, and responding to traffic congestion. Although logistical challenges--such as gaining approval to develop and upgrade infrastructure--are expected, real-world testing will provide important insights into how our systems can be improved.  
The current study focuses on small settlements, but expanding the system to control large cities with dense settlements and traffic congestion will bring additional challenges. Urban areas with larger traffic patterns and higher PSO-GWO volumes will require algorithm optimization to coordinate multiple intersections at the same time, ensuring traffic flows efficiently through the network. Future research could test the system's performance in major cities through simulations that combine real-time data, signal alignment, and dynamic traffic adjustments to determine its scale and efficiency.  
In real-world traffic systems, challenges such as sensor failures or network delays can significantly impact performance. The approach to improving PSO-GWO should include strategies to address these issues and ensure reliability. For example, sensor failure can be reduced by increasing redundancy through multiple sensors or other detection methods such as cameras or GPS data. In addition, the system can switch to backup mode with conventional traffic in the event that a sensor fails. Network delays can be addressed by using statistics to calculate traffic patterns when data are delayed in real time, ensuring smooth operation. Future projects could explore dynamic reconfiguration to accommodate sudden changes, such as accidents or road closures, by prioritizing emergency routes and redistributing traffic.  
The current system was designed primarily to improve traffic flow using a hybrid PSO-GWO system, with no specific attention paid to pedestrians or emergency vehicles. While our focus is still on improving traffic efficiency through machine learning and optimization, future developments could include features that separate and prioritize emergency vehicles such as ambulances and fire trucks. Such augmentations could include sensors or communication systems that detect pedestrian activity and emergency vehicles, prompting algorithms to adjust traffic signals in order to accommodate people and diffuse congestion in situations of emergency.