## 2 Literature Review
The system uses a central controller to make initial decisions based on global traffic conditions, selecting the nearest emergency center and the best route for the EMV. The SD-TLP system works by detecting accidents, selecting the nearest emergency center, choosing the best route, scheduling traffic signals, and restoring traffic signals after the EMV has passed through the intersection. The authors evaluated the SD-TLP system using cement in the city of Tabriz, Iran. The results showed that the system can significantly reduce EMV response time while reducing traffic congestion. The SD-TLP system has many advantages, including the ability to reduce both EMV response time and the adverse effects of traffic, its ease of implementation, and its scalability. However, the SD-TLP system also has some limitations, such as the need for a central controller, which may be difficult to implement in large cities and may not be effective in areas with heavy traffic. The authors of [14] propose an IoT-based traffic management system with which to address traffic congestion and safety issues. The system uses IoT sensors, communications technology, and data analytics to improve traffic flow and reduce congestion, fuel consumption, and emissions. While the framework shows potential, it lacks specificity in its implementation and may face challenges in scalability, data privacy, and integration with existing infrastructure. The article does not specify the types of sensors, communication protocols, or data analysis algorithms that will be used within the system. Additionally, the article does not discuss the potential costs and benefits of implementing such a system. Such a system may encounter difficulties when scaled up to large cities and may struggle to ensure data privacy and security; its integration alongside existing traffic infrastructure may be complex. The authors of [15] propose a new ITS model that is capable of predicting multimodal vehicle movement using machine learning and deep learning algorithms. The model includes data collection, algorithm implementation, and performance metrics using R-squared, RMSE (root mean square error), and MAE (mean absolute error). Different algorithms such as random forest, LSTM (long short-term memory), linear regression, and bagging are combined, resulting in a high efficiency of 93.52%. While the study demonstrates the feasibility of the proposed system, it does not contain detailed information about data collection methods, algorithm parameters, and actual testing. The authors of [16] propose a new traffic control system using piezoelectric sensors at T-junctions. In detecting the presence of multiple vehicles, the system aims to improve traffic flow and reduce congestion. The system consists of a piezoelectric sensor, a micro-controller, LEDs, and other components. Although the project shows potential, it lacks specific details about sensor placement, communication protocols, and algorithm implementation. In addition, this study did not address potential challenges such as sensor noise, environmental factors, and integration with traffic management systems. Further research is needed to address these limitations and evaluate the effectiveness of the proposed system in real traffic situations. In [17], the authors present a new approach to urban mobility that integrates unmanned aerial vehicles (UAVs) and visible light communication (VLC) technology into a smart city's traffic management system. They highlight the inadequacy of traditional traffic control methods in handling higher volumes of traffic in urban areas. Drones incorporating the YOLO algorithm are used to detect traffic jams in real time through photography and analysis. This information is then transmitted to traffic lights, allowing for dynamic adjustment. Visible light communication (VLC), as a form of communication existing between drones and traffic signals, has been proposed as an energy-efficient alternative to radio frequency-based systems.