## 2 Related Work  
Intersections tend to become the bottlenecks of the traffic flow, especially in the urban environment, where the traffic density is usually greater. Traffic management at the intersections, therefore, takes great importance. However, even in big cities all over the world such as New York, Dublin, or Porto, barely one quarter of the intersections are governed by traffic lights [14]. Furthermore, the operation of the traditional traffic lights systems causes significant delays [15], as they usually work under fixed parametrization rather than adapting to the traffic conditions. Notable early proposals aimed at improving the effectiveness of the traffic lights relied on Artificial Intelligence, starting from fuzzy approaches. In Ref. [16], for example, a fuzzy logic controller was implemented in an isolated intersection and compared with a conventional controller, achieving better results in terms of average delay of individual vehicles. Taking the proposal one step further, the authors of [17] devised a distributed approach that adapted the signal timing of the traffic lights by managing data about local traffic conditions and the timing of adjacent intersections, which yielded significant reductions in waiting times and number of stops in a Manhattan grid scenario. Other authors have proposed solutions based on Artificial Neural Networks (ANN), addressing the non-linearity characteristics of the vehicular traffic and trying to find an efficient and self-adapting management. An approach based on ANN was presented in [18] that used real-time traffic information to dynamically generate the best timing plan, obtaining improvements in a Manhattan grid of 3 \(\times\) 3 intersections. When V2V and V2I communications came into scene, a traffic-lights-to-vehicle communication protocol was proposed in [19] reduce fuel consumption, by broadcasting the scheduling information of the traffic lights so that the vehicles could adapt their speed trying to avoid stopping at a red light. These and other approaches can work well around intersections already covered by traffic lights, at the expense of infrastructure investments that could happen anyway as cities around the world turn to smart cities. However, as mentioned above, the percentage of intersections managed by traffic lights is very low, implying that the idea of making the real traffic lights smarter can only have limited impact. Virtual Traffic Lights promise to remove the dependence on an infrastructure, and therefore bring the benefits of new technologies to every intersection. Taking V2V capabilities for granted, the idea is that the vehicles work together towards self-organizing intersection management, so that the timing of the traffic lights is negotiated according to a certain protocol and disseminated around the intersection in real time. The most relevant proposal was presented in [14], and then refined and field-tested in [20]. In this approach, a vehicle is chosen cooperatively as the VTL leader of an intersection, and from that moment on it is responsible for VTL coordination and dissemination, until it leaves the intersection and a new cycle starts. The simulation results of [14] showed that, in cities with a reduced number of traffic lights, the VTLs can reduce traffic congestion up to 60% at high vehicular densities. The study of driving patterns has become an issue of great research interest since traffic congestion started to be a problem, already in the first decades of the 20 th century [21]. The research community began to perceive the benefits of the platooning of vehicles in the 70s. For example, the author of [22] tried grouping buses together in a platoon to facilitate flow through a central business district in New York during peak hours. However, it was not until the 90s that the first technology-enabled platooning systems appeared.