## 2 Literature Review
For example, one method demonstrated improvements in traffic precision, reduced travel times, and alleviated congestion using both simulations and real-time experiments. Traditional traffic light operations frequently fail to adapt to varying conditions, which causes congestion and delays on the roads. This method improved traffic flow and safety, demonstrating the potential to change the way traffic is managed in urban areas. However, the study highlighted the need for better predictive models and the challenges of implementing such a system on a broader scale. Reference [10] proposes a new quantitative linear quadratic (QLQR) control system integrated with software-defined networking (SDN). The QLQR component improves traffic signal control at intersections, while SDN prevents congestion by adjusting signal timing. By constantly monitoring traffic, the system aims to reduce queue length, reduce waiting times, and improve vehicle availability and speed in real time. The proposed SDN-QLQR system is validated through simulation using the SUMO tool with traffic data from an Indian city. The results show significant improvements in performance metrics such as line length, wait time, throughput, and vehicle speed compared to other state-of-the-art methods, including LQR (linear quadratic regulator), MP (max-pressure), DTLC (dynamic traffic light controller), and SOTL (self-organizing traffic lights). The SDN-QLQR system, although promising in the simulation, lacks robustness. It does not address connectivity, latency, or different traffic conditions. The study also failed to address the cost and effectiveness of implementing such a system within large urban networks. Relying on SUMO simulations alone may not adequately capture their physical complexity. Future work should focus on empirical testing and addressing these limitations to ensure effective implementation of this framework. Reference [11] highlights the potential of computer vision-enhanced traffic lights to improve traffic management in Kazakhstan. By analyzing real-time data from sensors and cameras, these smart traffic signals can optimize traffic flow, reduce congestion, and enhance overall urban efficiency. While the proposed approach offers several advantages, it is important to consider the limitations and challenges associated with its implementation. Further research and adaptation may be necessary to ensure its effectiveness in diverse urban environments. In [12], the authors propose a traffic light system (DTLS) that uses computer vision to improve traffic in cities. The system uses the YOLO object detection algorithm to estimate the number of vehicles on the road and adjust traffic signals accordingly. This helps to reduce congestion and improve traffic flow. The system also includes a method for prioritizing traffic based on delays experienced by vehicles at different intersections. Within this model, priority is given to traffic arriving from areas with long waiting times, which can help reduce overall congestion. In addition, the system provides a green light for emergency vehicles. This ensures that emergency vehicles can pass through roads without delaying traffic. The system is designed to be energy-efficient and uses wireless communication technology, which reduces costs and increases sustainability. Overall, DTLS has the potential to improve traffic flow in urban areas. By using computer vision and intelligent algorithms, the system can help to reduce congestion, improve traffic flow, and reduce emissions. The article's DTLS system, although promising, faces potential weaknesses such as inaccurate object detection, overhead, communication delay, cost, and privacy concerns. These things can hinder its effectiveness and real-world implementation. Reference [13] details a software-defined traffic light system (SD-TLP) to improve the response time of emergency medical vehicles (EMVs) in urban areas.