## 2 Related Work  
This section discusses the approaches, techniques, technologies, and tools that have been suggested to overcome issues in traffic management. In the recent past, studies have been conducted to highlight many issues related to smart cities and these have emphasized the need for smart-traffic management [7, 8, 9]. To achieve this, many proposals have been made. In one such study, the use of data mining for classifying roads into six categories according to the traffic density (free, low, mid, high, very high, and extreme) was proposed [10]. Depending on the speed of vehicles and the average waiting time, this study predicted the expected congestion on a road and notified the end-user to select the appropriate route. Another study was able to predict traffic congestion by relating travel times with the vehicular load on the route [11]. Similarly, a study proposed a predictive road traffic management system based on the vehicular ad hoc network (VANET) architecture [12]. An algorithm to estimate the speed of a car from a video feed based on the image scale factor was also proposed [13]. More methods for addressing this issue using image processing can be found in [14]. Furthermore, techniques based on image processingand deep leaning for remote sensing images can be found in [15]. An overview of image processing algorithms and traffic management issues is presented in [16]. Applications of artificial intelligence (AI) in transportation systems have also been discussed in [17]. Ref. [18] studied the speed, acceleration, and direction of traffic flow using deep learning to manage the traffic flow. In contrast, many studies have been conducted to investigate the role of sensors in traffic management. One study suggested deploying WSN and RFID on roads to create dynamic traffic lights [19]. However, the accuracy of these devices is affected by factors such as lighting, the random movement of vehicles, etc. Similar work, presented in [20], argued that with the help of a real-time intelligent central control unit, medical emergencies can be handled by pausing traffic in any lane to give priority to ambulances. This can be achieved by relying on RFID in cars and traffic lights. More such work on the application of sensors and RFID in smart traffic management can be found in [21]. A proposal to use scheduling algorithms to select the optimal path for an autonomous vehicle (AV) and suggestions to prepare special paths for this type of vehicle is discussed in [22]. Furthermore, an overview of AV is presented in [23]. However, the initial cost of new infrastructure is a major hurdle. The authors of one study [24] used micro-controllers and infrared (IR) sensors to detect traffic density and control the traffic signal to reduce traffic congestion and avoid unnecessary waiting times. A signal control project based on radio sensors with an Arduino low-power micro-controller has also been developed [25]. This controller works to capture and process information about traffic flow on roads in order to dynamically control the waiting time on the red light of a traffic signal. In a similar study, a method was presented of estimating the time of vehicles arriving at traffic lights based on their speed to inform drivers about street traffic status using computers in their cars [26]. Studies have also investigated the use of sensors for detecting accidents and traffic violations. One of the traditional ways to detect traffic violations is the use of surveillance cameras and radars. However, their accuracy is affected by weather conditions such as fog, heavy rain, and so on. Many researches have used modern technologies such as wireless devices, and drones to overcome these limitations of existing methods [27; 28; 29; 30]. Generally, vehicles involved in an accident block the route, which leads to congestion. A European project employed a set of radio sensors to monitor the blocked and moving vehicles [31].