# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 2 Related Work
### Vehicular Traffic Prediction  
An important step in vehicular congestion avoidance is the prediction of traffic. Many vehicular traffic prediction approaches from the literature use data mining and simulation for the purpose of predicting short-term vehicular traffic prediction in non-urban areas' contexts. A comprehensive review in this matter was performed in [8]. The work in [22] proposed a solution to predict the fundamental traffic diagram parameters [23; 24]. Urban traffic prediction solutions are fewer than for non-urban ones. The solution in [25] proposed a solution to predict travel time by using a large amount of traffic information from Berlin and Thessaloniki. In addition to traffic prediction approaches, there are more specific solutions used to predict vehicular traffic congestion in order to reduce the time spent in vehicles. In this regard, the authors in [26] propose an unsupervised incremental learning solution that can be used to detect and profile vehicular traffic congestion. They predicted short-term traffic by using hundreds of millions of vehicular movements obtained from intersections in the State of Victoria from Australia. A promising solution for evaluating congestion was proposed in [27]. Their strategy is based on geocoding vehicular traffic events that come from Twitter in order to obtain a training data set. The training data set is to obtain a prediction model by applying a support vector machine method. This approach was used to generate a spatio-temporal vehicular traffic data set in order to analyse vehicular traffic congestion in Mexico City. The solution proposed in [28] is based on various re-routing mechanisms used to avoid vehicular traffic congestion. For their experiments, they simulated one thousand vehicles by using SUMO [29] and TraCI [30]. In [31; 32], an inter-vehicular communication based solution is proposed to avoid vehicular traffic congestion in urban areas. For the work in [31], the New York map was used for traffic simulations and measurements, while for the one in [32], the evaluation was performed in Colima city from Mexico by combining inter-vehicular communication, infrastructure-to-infrastructure connectivity, fixed roadside infrastructure and a large amount of data. The authors in [33] propose a vehicular traffic congestion avoidance method based on vehicle re-routing in the context of synthetic grid maps. Their method wants to provide an optimal vehicular guidance suggestion to bypass congested roads as soon as possible. The impact of humans on traffic flow was evaluated in [34], and it was concluded that traffic stability is significantly impacted by humans and, therefore, the risk of traffic breakdowns is imminent. The study in [35] proposes a hybrid deep learning model to predict vehicular traffic flow. In order to automatically determine the importance of input parameters of large scale space-time points, they introduce an attention model aimed to measure the importance of input traffic flow of past spatial-temporal position in correlation with the future traffic flow. A noteworthy vehicular traffic modelling approach is described in [36]. Their approach models and analyses vehicular traffic at a specific signalized road intersection by using an artificial neural network (ANN) model based on traffic flow parameters such as vehicular speed, time of the day, traffic volume, and class of vehicles. A special category of traffic congestion is the one that is generated by traffic incidents. The studies in [37; 38; 39; 40] propose approaches that can be used to improve the urban traffic congestion prevention, especially in the context of signalized intersections. In addition to comprehensive work about traffic simulation benefits and challenges in the context of TraffSim [41; 42], a noteworthy study on vehicular traffic prediction and congestion reduction through route computation, was presented in [43]. It simulates between 2000 and 3000 vehicles on real and synthetic maps.