## 2 Related Work  
Traditional traffic management systems are based on centralized infrastructures in which cameras and sensors placed along the roadside collect information about vehicle density and the traffic state. These data are sent to a central unit that processes them and makes appropriate decisions. This type of framework is very costly in terms of deployment and is characterized by the long time needed to process information. The rapid development of wireless communication has raised the interest for car manufacturers, the research community and governments, making a new decentralized architecture based on vehicle-to-vehicle communications. Vehicular _ad hoc_ networks are a specific type of mobile _ad hoc_ network (MANET) [1] in which the nodes are vehicles. The road-constrained characteristics of these networks, the high mobility of the vehicles, their unbounded power source and the presence of roadside wireless infrastructures make VANETs an important research topic. The main purpose of VANETs is to be a platform that can support intelligent inter-vehicle communication, improving traffic safety, although they are also useful for traffic management and other services, e.g., Internet access. The presence of city infrastructure in emerging smart cities has an important impact on urban communications. In some applications, one must find a route to the closest access point (AP), e.g.,connecting to the Internet. In a city with advanced wireless infrastructure deployed, it would take only a few hops to reach the nearest AP under day-traffic conditions. Roadside access points should be placed in special locations, such as on traffic lights, since they are well positioned to act as traffic routers. They already form a traffic grid, usually located where traffic is most intense, and are equipped with a power supply and directly maintained by local municipalities. For instance, [4] proposes a priority intersection control scheme in a self-organized manner, so that smart traffic lights detect the presence of emergency vehicles and assign them a priority at intersections. The goal is to provide a \"green-wave\" signal display for the emergency vehicles seeking to avoid accidents. Several proposals of routing protocols for VANETs have been presented. Research works claim that the best routing protocols for VANETs are those that use geographic routing, which are based on the knowledge of the instantaneous locations of nodes [5]. GPSR (greedy perimeter stateless routing) [6] is a well-known geographic routing protocol specially designed for VANETs. It forwards packets to the neighbor node that is closest to destination following a hop-by-hop scheme. GPSR uses two different techniques to forward packets: _greedy forwarding_, which is used by default, and _perimeter forwarding_, which is used whenever greedy forwarding cannot be used. Since nodes require knowing their neighbors' positions, they periodically transmit a hello message containing their own identifier (e.g., IP address) and their position. Several proposals that improve GPSR have been presented in the literature. The authors in [7] proposed movement prediction-based routing (MOPR), which improves the routing process of GPSR by selecting the most stable route in terms of lifetime with respect to the movement of vehicles. In [8], an algorithm was proposed that modifies GPSR and exploits information about movement to improve the next forwarding node decision. The authors use information about position, moving direction and speed to make routing decisions. The proposed protocol was compared to GPSR in a highway setting, showing improvement. Advanced Greedy Forwarding (AGF) [9] improves the performance of GPSR, since its forwarding technique is more fault-tolerant than the traditional greedy forwarding. Recently, some proposals of routing protocols for VANETs that include several metrics have been presented. The use of alternative metrics in VANETs in addition to the classic distance has shown notable benefits.