## 2 Literature Review  
Reference [6] used the Welch-Powell algorithm to operate traffic lights at intersections in Bandung and focused specifically on optimizing signal timing based on traffic capacities, thereby refining traffic operation and reducing wait times. Subsequent analysis of traffic data established that the algorithm significantly improved traffic light accuracy, which is also important in reducing the time users spend on the road. Precisely, at the Pasteur Interchange intersection of Bandung, the algorithm served to optimize the durations of traffic lights, improving on the scheme currently employed by the Bandung transportation office; it reduced the number of waiting cars and refined the movement of traffic more broadly. This method was applied to decrease the volume of traffic and increase safety, which led to economic and social benefits. However, the study was limited by its dependence on precise and complete traffic data and the need to precisely parallel local traffic designs, intersection features, and road structures for successful application, all of which compromise its generalizability; that said, the study did produce valuable insights into traffic regulation in similar areas. Reference [7] used real-time traffic data and IoT sensors to observe the negotiation of traffic and the movements of emergency vehicles, demonstrating that integrating the IoT can streamline the response of emergency vehicles through more effective navigation. The aforementioned IoT-enabled traffic management system improved the provision of emergency services by reducing waiting times, increasing safety, and optimizing resources. However, the potential variation in traffic operations and differences in infrastructure between areas may limit the applicability of this study. These conclusions build upon broader suggestions for improving IoT-based traffic management in urban areas and highlight the need for further research on the benefits and limitations of this technology. The work underlines the potential of green lights to transform the movement of emergency vehicles and the management of traffic. By reducing waiting times, this IoT method can save lives, minimize car damage, and enhance the accuracy with which emergency services are provided. As this method is developed, more surveys and IoT-based designs--such as Green Lights Ahead--may lead to smarter, more effective area transportation systems. This study demonstrates the integration of IoT technology into traffic management, which especially benefits emergency response vehicles. IoT sensors have helped to improve the navigation and coordination of emergency vehicles by improving traffic signals in real time. Despite achieving good results in urban areas, this system faces limitations related to the diversity of infrastructure in some areas, indicating the need for further research to discover the full potential of IoT-based traffic management systems. Reference [8] uses NodeMCU ESP8266, IR sensors, and the Firebase database to advance the detection of cars and reduce waiting times caused by traffic congestion. When detecting an emergency, the light turns green immediately and remains green for the following 10 s to clear the street, thereby allowing the car to pass through without congestion. The model prototype successfully achieved its goals and could be controlled via the Firebase dataset, which also allowed for manual operation in the event that the sensor failed. Although this method proved to be effective, it faces challenges related to the sensor's sensitivity to the vehicle's distance and speed, which require further research. Reference [9] integrated advanced machine learning methods to develop traffic operation and road safety via smart traffic lights. These methods used real-time traffic monitoring and applied an algorithm for adaptive signal control in response to automated traffic light sequences based on current conditions.