# UAV-Assisted Hybrid Scheme for Urban Road Safety Based on VANETs
## 2 Related Works  
For controlling congestion, current-time information sharing is very important. This becomes a significant challenge for sharing current-time information in VANETs due to the imbalance of traffic flow on the road network and sudden obstructions, blockages, or traffic accidents. To achieve a mechanism for the current-time sharing of information in a fast-moving node transportation network, a road-based vehicular traffic (RBVT) routing through VANETs presented in [19]. In [6], the authors present a method for estimating travel time from a source place in a road network to a destination place called Travel Time Assessment (TTE), which serves as a route planning metric. They proposed a current-time route planning algorithm based on TTE comparisons by collecting data to avoid traffic congestion in an intersection. In [20], to improve the connectivity of intersection-based routing, the authors have introduced the connectivity of junction-based routing (CJBR). Like other routing protocols focused on intersections, CJBR disregards dependence on traffic density metrics for the selection process of intersections during the development of routing paths in urban environments. The author has designed a flooding model in [21] that automatically responds to any change in topology while overcoming current obstacles. At the same time, in an ad hoc fashion, exchange data with a drone, commonly known as an unmanned aerial vehicle (UAV) in this work, explores well-regulated routing paths that can provide long-life connectivity. UAV's assistance to vehicles will provide a high transfer rate and low transfer latency, while effectively extending the network link, depending on the amount of traffic and the end time of each discovered path. In [PERSON] et al. [22] other functions are introduced to enhance the efficiency of its proposed algorithm, including (1) Using the data in the extended neighboring table for optimal route selection, and seek to prevent neighbors from reaching local maximums on each destination route and avoiding the loop of data packets. (2) Substitute the constant greedy forwarding with the right-hand rule in recovery mode, and the left-hand rule fused at the same time. [PERSON] et al. [23] proposed an optimization model, which reduces the amount of total travel time on the road network and the time representation of the system's impact of traffic imbalances. The author introduces the analytical expression of the problem of optimization and proposes an algorithm, the dynamic interior point method (DIPM), which by rerouting the driver solves this problem of optimization. In [24], the author proposes an optimization model that not only increases traffic flow but also avoids traffic congestion to improve road utilization. The algorithm uses a real map for simulating the area, with a large amount of traffic data. In [25], the author provides an analytical framework to study UAV-to-UAV communication underlay with the uplink of a cellular network. In their work, they proposed a realistic channel model, antenna pattern, and power control strategy equation obtained. In [26], the author proposed a protocol named early congestion detection and control routing protocol (EDAODV). This protocol is applicable to one-way highways and contrasted to AODV protocol because it is non-adaptive to routing congestion in VANETs. However, previous congestion control methods can not resolve the effects of the present traffic conditions, such as the traffic flow on-road segments and the waiting queue at the intersections. Based on the UAV-assisted traffic congestion control method, there are several technologies and algorithms based on intersection congestion control methods in VANETs. In [27], the author proposed the UAV-assisted VANETs routing protocol (UVAR) and its extensions to support self-organized routing between VANETs and UAVs as well as UAVs themselves. The UVAR protocol is designed for urban scenarios, and its primary goal is to enhance the routing efficiency based on UAV's knowledge of ground road traffic.