## 2 Related Work
Despite showing very good results in terms of prediction, the proposal does not include redirection strategies in case of congestion. Another point to consider is that this model works in a single street segment, which does not allow to determine its efficiency on a global scale (the entire vehicular network). From the perspective of machine learning, traffic prediction is a spatio-temporal sequence prediction ([PERSON], 1999). Recently, long short-term memory networks (LSTM) have gained popularity as a tool for the prediction of spatio-temporal sequences ([PERSON], 1999). LSTM are a special type of recurrent neural network (RNN) that have been shown to be able to learn long-term dependencies ([PERSON], 1999; [PERSON] and [PERSON], 1999; [PERSON] and [PERSON], 1999) introduced them. The RNNs work very well with a wide variety of problems and, because of this, are widely used today. LSTMS are specifically designed to avoid the problem of long-term dependency. Each cell acts as an accumulator of state information and controlled by a set of cells that determines the flow of information. There is a gateway that allows you to control whether the incoming information is going to be accumulated or not. There is also an oblivion door, which allows us to determine if the state of the previous cell will be taken into account. In addition, an output cell determines whether the output of the cell is to be propagated to the final state. The cell type and control gates are the most important advantage of the LSTMs since they get the gradient trapped in the cell and prevent it from disappearing too fast, which is a critical problem of the basic RNN model ([PERSON] and [PERSON], 1999). [PERSON] et al. (2000) developed a multivariable version of LSTM that they called ConvLSTM, which has convolutional structures in both state-to-state and state-to-state transitions. By stacking multiple ConvLSTM layers, they were able to build a model that proved to have excellent spatio-temporal sequence prediction capability, suitable for making predictions in complex dynamic systems. DIVERT ([PERSON] and [PERSON], 1999) is a distributed hybrid vehicle redirection system to avoid congestion. This system uses a server and Internet communication to determine a precise global view of traffic. Although, DIVERT presents improvements in ATT, the comparison is made with respect to a centralized re-routing system and does not make any comparison against a scenario without any strategy. Next route routing (NRR) ([PERSON] and [PERSON], 1999) extends the functionality of the SCATS system (Sidney Coordinate Adaptive Traffic Systems) by adding vehicle congestion and re-routing detection mechanisms. The re-routing decision is made taking into account the travel destination and the current local traffic conditions. The proposed architecture allows the positive impacts of re-routing to spread over a larger area. The results obtained show a reduction of 19% in ATT. It is necessary to emphasize two points on this proposal. First, it is an extension of a previous system (SCATS), it is not an autonomous system. Second, it requires that all vehicles provide information to know the real state of the traffic.