# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 3 Vehicular Traffic Congestion Avoidance Methodology  
Congestion avoidance based on the vehicular traffic thresholding mechanism (CAVTTM) integrates in SUMO the KEP tree data structure that we developed in [8] and proposes a novel vehicular traffic thresholding mechanism in order to reduce or avoid congestion in urban areas. To evaluate the efficiency of CAVTTM, we generated and evaluated a number of relevant urban vehicular traffic scenarios by combining the parameters below:  
* Vehicular density represented by the number of simulated vehicles;
* Road network topology classified in four types: Grid Topology, Historical/Irregular Topology, Unbalanced Grid Topology, Hybrid Topology;
* Infrastructure settings represented by the average traffic light delay, which represents the average time spent at each traffic light (seconds). Thus, based on the above parameters, we define the tuple <vehicular density, road network topology, infrastructure settings> used by our methodology. The defined tuple is employed by the proposed methodology, as shown in Figure 1. The route computation algorithm uses data stored statically in the map (e.g., segment length, speed limit, and vehicular infrastructure, number of lanes) and infrastructure information (e.g., traffic light delay) to compute routes. The road network topology is based on the urban area used to compute routes and influences the output of the route computation algorithm. We define four types of road network topologies as follows:  
* _Grid Topology_ representing almost ideal urban areas with dense road topology in the form of a grid, surrounded by highways and a proportional surface;
* _Historical/Irregular Topology_ representing urban areas with a small density of road infrastructure following random geometry based on historical roads and proportional surface;
* _Unbalanced Grid Topology_ representing urban areas with long roads, highways, grid topology, disproportional surface and pedestrian areas;
* _Hybrid Topology_ representing urban areas with very rich road infrastructure that has many highways, large intersections and a proportional surface. The route computation algorithm outputs the computed routes which, at their turn, generates the vehicular traffic density to be considered during future route computation. For the vehicular density, we define _free flow_ for a range of [10 K+...30 K+] simulated vehicles and _congestion_ for a range of [50 K+...100 K+] simulated vehicles. Both free flow and congestion are generated on urban areas of 65 km [2]. In is noteworthy that, in addition to the comprehensiveness of the road network topologies, our focus is to reach a very high congestion by having up to 49 K concurrent vehicles that run on a road network. To the best of our knowledge, this is the highest number of concurrent vehicles simulated in a road network in order to model traffic congestion. The traffic thresholding mechanism is a novel concept introduced in this study and is employed in the route computation algorithm to predict possible traffic congestion and to propose alternative routes in order to avoid congestion as much as possible. In case of possible traffic congestion, the thresholding mechanism increases the effort on a segment and, therefore, the route computation algorithm searches for other lower effort segments in order to balance the vehicular traffic. The congestion avoidance solution we propose is described in the flow diagram shown in Figure 2.  
To provide an effective, scalable and reliable solution for congestion avoidance, we designed a layered flow based on a set of successive steps defined below:  
* S1--Generation of the start and destination points for each vehicular route in the context of a selected road network topology from a comprehensive set of road network topologies;
* S2--The output of S1 is consumed by the route computation algorithm to obtain routes that reduce or avoid vehicular traffic congestion. During route computation, the proposed thresholding mechanism targeting vehicular traffic congestion avoidance is employed.