## 2 Related Work  
CoTEC [6] (Cooperative Traffic congestion detECtion) is a cooperative vehicle system based on vehicle-to-vehicle (V2V) communication. This system uses fuzzy logic to detect local traffic conditions, using beacon messages received from surrounding vehicles. When detecting congestion in the area, CoTEC activates a cooperative process that shares and correlates the individual estimates made by the different vehicles, thus achieving a characterization of the degree of traffic congestion. Tested in large scenarios, COTEC obtained good results in the detection of congestion; however, it does not integrate any vehicle re-routing strategy. [PERSON] et al. [7] present a system, based on CoTEC, called CARTIM (identification and minimization of traffic congestion of cooperative vehicles). CARTIM, like CoTEC, uses fuzzy logic to characterize road traffic, adding a heuristic that seeks to reduce drivers' travel time by allowing them to modify their travel routes, thus improving vehicle flow in the event of congestion. The authors report that CARTIM achieves a reduction of 6% to 10% in ATT. Urban CONgestion DEtection System (UCONDES) [8] is another proposal based on V2V communications that use an artificial neural network (ANN) designed to detect and classify existing levels of congestion in urban roads. The proposal also adds a mechanism that suggests new routes for drivers to avoid congested areas. The ANN uses the speed and traffic density of a street as input to determine the existing level of congestion: light, moderate, and congested. The vehicles receive the classification obtained, as well as a new route, through beacon messages so that the vehicle can alter its current route and thus avoid congested roads. The simulations carried out in a realistic Manhattan scenario showed a reduction of 9% to 26% of ATT. SCORPION [9] (A Solution using Cooperative Rerouting to Prevent Congestion and Improve Traffic Condition) uses vehicle-to-infrastructure (V2I) communications. All vehicles send their information (id, current position, route, and destination) using 4G and LTE technologies to the nearest road side unit (RSU), which uses the k-nearest neighbor algorithm to classify street congestion. After determining the traffic conditions, a collective re-routing scheme plans new routes to those vehicles that will pass through congested areas. Like the UCONDES system, SCORPION performed simulations using a realistic Manhattan scenario, achieving a 17% reduction in the ATT. The aforementioned proposals present some limitations, such as a limited re-routing distance, no real-time mechanism for the detection of congestion or no mechanism for re-routing with a global perspective. [PERSON] et al. [5] propose a centralized system that uses the data obtained in real-time of the vehicles (position, speed, and direction) to detect traffic congestion. Once the congestion is detected, the vehicles are redirected according to two different algorithms. First, the shortest dynamic path (DSP), which re-routes vehicles using the shortest routes that also, has the lowest travel time. The authors also mention that a defect of this algorithm is the possibility of moving the congestion to another point. To solve this, a second algorithm, random k-shortest path (RKSP), randomly chooses a route between k shorter routes. The objective of this algorithm is to avoid switching the congestion from one place to another and, in this way, to balance the redirected traffic between several routes. This scheme does not implement a mechanism in real time to detect congestion as it occurs; it is only able to detect it during the next phase of re-routing. One way to improve the above mentioned solutions, it is to forecast the state of vehicular traffic and, in this way, anticipate congestion. [PERSON] (1999) proposes a traffic prediction system using the Kalman filtering technique. The predictive model uses historical and real-time information when making the prediction.