# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 2 Related Work
### Large Scale Urban Traffic Congestion Simulation  
The discussion and analysis in the previous subsection were about prediction of vehicular traffic and avoidance of vehicular traffic congestion, especially in urban areas. In this subsection, the large-scale urban traffic congestion simulation approaches proposed by different authors are discussed. To model real-world traffic congestion scenarios in urban areas through simulation, we need to take into consideration the scalability of the model. A summary of the existing studies on urban traffic congestion simulation is presented in Table 1. For simplicity, in the aforementioned table and in this study, we use a \"K\" symbol when we refer to thousand. The vehicular network simulator (VNS), described in [15], uses a DIVERT 2.0 traffic simulator to represent traffic that simulates a total of 130 K vehicles, having a maximum of 15 K concurrent vehicles running on the road network. Their methodology is based on quad tree data structure that is used to store and represent a large number of vehicles on the Porto City road network. The authors of study in [16; 17] use the INTERGRATION to accurately simulate large-scale traffic on the road network of downtown Los Angeles. A total number of vehicles that run in their solution is more than 563 K, with a maximum of 30 K concurrent vehicles on the road network. Considering that the road network area used for the simulation was 133 km\({}^{2}\), they managed to simulate moderate congestion on the roads. The authors of [18] also use an INTEGRATION traffic simulator to model large-scale urban traffic. The total number of vehicles that run in their solution is more than 145 K, having 30 K concurrent vehicles on the road network in downtown Los Angeles. Because the road network has an area of 133 km\({}^{2}\), the simulated traffic mimics a moderate congestion scenario. Their vehicle simulation mechanism performed better than the approaches in [16; 17] because the vehicle positions were modelled using a grid cell data structure with an update index. In this case, the query operation has a linear performance with regard to the vehicular density, whereas we have a constant complexity for update operation. A bottleneck in this solution is represented by the query operation when the frequency of query requests is high, or the vehicular density is high (as in the vehicular congestion case). The work in [45] simulates traffic congestion with 2.2 K vehicles on a road network of 8 km\({}^{2}\) in Ottawa and 14 K vehicles on a road network of 4 km\({}^{2}\) in Cologne. The maximum number of concurrent vehicles on the road network was not specified. Another work that simulates traffic congestion in Cologne was described in [46] and simulated 46 K vehicles on a road network of 25 km\({}^{2}\) in Cologne. As in [45], they did not specify the maximum count of concurrent vehicles on the roads. The work in [47] generated low level congestion (between 1.25 and 6.25 K vehicles) on two road networks of 25 km\({}^{2}\) in Los Angeles and Paris. The Paris area is more prone to traffic congestion owing to the presence of more bridges. On the other hand, in [48], 7.2 K vehicles in 1 km\({}^{2}\) to generate high congestion are simulated. The works in [45; 46; 47] presented their percentage improvement regarding the time spent in traffic, as shown in Section 5.