# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 1 Introduction  
Nowadays, most of the urban areas face traffic congestion, which decreases the living standard and increases the time spent in traffic by drivers: extra time sitting in traffic (in terms of billions of hours), resulting in hundreds of billions of dollars costs [1, 2]. For example, as stated by the authors in [3], in major urban areas in the United States (US), the travel time during congestion is increased by 32%. In recent years, intelligent transportation systems (ITS) have attempted to provide solutions for traffic prediction and vehicular routes enhancements for the purpose of congestion avoidance. Many of these solutions are based on algorithms that use knowledge from a large spectrum of data. The large volume of data used to improve traffic in urban areas faces another problem, scalability, as is shown in [4]. Data sharing between vehicles affects scalability. More specific data sharing studies based on various architectures and communication methodologies are presented in [5, 6, 7]. In [8, 9], multiple scalable data models were used to represent vehicular data for traffic prediction and congestion avoidance. These approaches use a centralized architecture that stores and controls vehicular traffic information in order to globally reduce traffic congestion in urban areas. The prediction of ITS' vehicular traffic and the avoidance of vehicular traffic congestion are affected by the route computation process. The route computation process uses and affects vehicular traffic aspects that are reflected in the daily travel experience, as has been shown in studies such as [10, 11, 12, 13]. From an implementation cost perspective, traffic simulation is an efficient and reliable platform that has been used to model and run vehicular traffic in many studies [8, 9, 14, 15, 16, 17, 18, 19, 20]. The work in [8, 9] modeled vehicular traffic in a scalable manner, but the congestion avoidance improvement was below 3%. This is because the route computation algorithmuses a simple penalization model for traffic congestion. Moreover, the existing congestion avoidance solutions lack variety regarding the tested urban road network topologies, similar to the ones presented in [21]. In addition, in the existing solutions, most of the cases simulated low to moderate congestion but did not simulate high congestion. To summarize, all of the aforementioned studies lack at least one of the following properties:  
* Effectiveness of the solution regarding vehicular traffic congestion avoidance and traffic flow improvement;
* Scalability regarding the amount of vehicular traffic data representation and simulation;
* Comprehensiveness of the evaluated vehicular traffic scenarios. Considering the aforementioned achievements and challenges, we considered that it is valuable to design and develop a novel vehicular traffic congestion avoidance solution that noticeably improves moderate and high traffic congestion simulated on a set of comprehensive road network topologies. The main goal of this study is to develop a congestion avoidance solution based on a vehicular traffic thresholding mechanism that improves urban traffic congestion in a comprehensive set of simulated traffic scenarios. The main contributions of this study are as follows:  
1. A novel vehicular traffic congestion avoidance approach was used in a Vehicle to Cloud (V2C) simulated ecosystem;
2. A novel traffic thresholding mechanism was integrated into the route computation algorithm in order to compute routes that avoid traffic congestion generation. The routes computation was carried out on a comprehensive set of road network topologies representing real urban areas. For evaluation, we simulated vehicular traffic in SUMO based on the computed routes;
3. Defining and evaluating a set of testing scenarios for tuples: <vehicular density, road network topology, infrastructure settings>>. The remainder of this paper is organized as follows: The next section discusses work related to the existing literature.