## 2 Related Work
A route server that can handle all of the traffic in a city is supported by the introduction of the traffic-management paradigm [25]. By taking into consideration both current and projected levels of traffic congestion, the contribution balances traffic flows. The simulation research, which used actual data on traffic congestion in Valencia, Spain, showed how the suggested remedy would enhance traffic flow throughout an average day. According to experimental findings, the proposed traffic-prediction equation can significantly improve average travel speeds and travel times, two measures of reduced levels of congestion and increased traffic fluidity, when used in conjunction with frequent updates of traffic conditions on the route server. A novel approach for estimating statistical variables based on a unidirectional-road-network model was provided [26]. The suggested technique is more realistic, and it is a route-planning algorithm for periodically constrained search regions that generate virtual borders with a lower confidence level. A comprehensive experiment was carried out with the suggested method using the real road network in Zhengzhou. The suggested approach was compared to current methods. Based on the results, the suggested algorithm considerably increased search performance under the condition of the optimum path toward ensuring the best solution. To avoid congestion, the time-dependent vehicle-routing problem with time windows (TDVRPTW) was presented [27]. A mathematical model for the TDVRPTW was developed by considering time-dependent vehicle speeds, travel time, capacity, customer demand, wait time, time window, service time, the influence of dynamic loads, and the effect of travel speed on vehicle carbon emissions. Furthermore, a congestion-avoidance strategy was provided to prevent peak-hour traffic congestion and temporal traffic congestion. The TDVRPTW properties of the model were used to develop an enhanced ant-colony algorithm with a congestion-avoiding strategy (IACACAA). Several computational tests proved the efficacy of the proposed methodologies. Congestion avoidance decreased vehicle fuel consumption and carbon emissions while protecting the environment. Energy-efficient message distribution (EEMD) based on a fog-assisted congestion-avoidance method for the IoV is described in [28]. In contrast to the majority of existing strategies, EEMD uses the advantages of fog computing to reduce communication costs and manage services. The status of every vehicle must be routinely updated to a fog server, either directly or through intermediary nodes. An enhanced RES-YOLO detection technique was presented for automated vehicle detection [29]. The proposed method enhances the detection performance of the conventional YOLO method by selecting optimum-feature networks and constructing adaptive-loss functions. The YOLO deep-learning vehicle-identification model was created and was then contrasted with current sophisticated target-recognition techniques. This study seeks to create a path from the present to the future by establishing an efficient path. Compared to the current protocol, it produces the shortest route, thereby reducing packet loss, increasing throughput, and decreasing latency. By avoiding congestion, all current routing algorithms attempt to increase throughput and decrease end-to-end latency. The suggested method, on the other hand, increases throughput by decreasing end-to-end delay and increasing energy economy. Furthermore, the suggested algorithm not only manages congestion but also detects it automatically. Table 1 summarizes and compares the previously mentioned contemporary approaches.