Congestion in traffic is an unavoidable circumstance in many cities in India and other countries. It is an issue of major concern. The steep rise in the number of automobiles on the roads followed by old infrastructure, accidents, pedestrian traffic, and traffic rule violations all add to challenging traffic conditions. Given these poor conditions of traffic, there is a critical need for automatically detecting and signaling systems. There are already various technologies that are used for traffic management and signaling systems like video analysis, infrared sensors, and wireless sensors. The main issue with these methods is they are very costly and high maintenance is required. In this paper, we have proposed a three-phase system that can guide emergency vehicles and manage traffic based on the degree of congestion. In the first phase, the system processes the captured images and calculates the Index value which is used to discover the degree of congestion. The Index value of a particular road depends on its width and the length up to which the camera captures images of that road. We have to take input for the parameters (length and width) while setting up the system. In the second phase, the system checks whether there are any emergency vehicles present or not in any lane. In the third phase, the whole processing and decision-making part is performed at the edge server. The proposed model is robust and it takes into consideration adverse weather conditions such as hazy, foggy, and windy. It works very efficiently in low light conditions also. The edge server is a strategically placed server that provides us with low latency and better connectivity. Using Edge technology in this traffic management system reduces the strain on cloud servers and the system becomes more reliable in real-time because the latency and bandwidth get reduced due to processing at the intermediate edge server.

翻译：交通拥堵是印度及其他国家许多城市不可避免的状况，这是一个值得高度关注的问题。道路上汽车数量的急剧增长，加之陈旧的基础设施、交通事故、行人交通以及交通违规行为，都加剧了交通环境的严峻性。在这种恶劣的交通条件下，迫切需要自动检测与信号系统。目前已有多种用于交通管理和信号系统的技术，如视频分析、红外传感器和无线传感器。这些方法的主要问题是成本高昂且维护需求高。本文提出了一种三相系统，能够根据拥堵程度引导应急车辆并管理交通。在第一阶段，系统处理捕获的图像并计算用于发现拥堵程度的指标值。特定道路的指标值取决于其宽度以及摄像头对该道路的成像长度。在系统设置时，需要输入参数（长度和宽度）。在第二阶段，系统检测任何车道中是否存在应急车辆。在第三阶段，整个处理与决策部分在边缘服务器上执行。所提出的模型具有鲁棒性，并考虑了雾霾、雾气和大风等恶劣天气条件，在低光照条件下也能高效运行。边缘服务器是一种战略性部署的服务器，能够提供低延迟和更好的连接性。在该交通管理系统中使用边缘技术，可减轻云服务器负担，并且由于在中间边缘服务器处进行处理，延迟和带宽得以降低，系统在实时应用中变得更加可靠。