# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 3 Vehicular Traffic Congestion Avoidance Methodology
SUMO was preferred over other vehicle simulation tools (e.g., INTEGRATION and OsmAnd) because of the following main aspects:

* Scalability: SUMO supports hundreds of thousands of simulated vehicles, similar to INTEGRATION, while OsmAnd supports only tens of thousands;
* Simulation time: SUMO is ten times faster than OsmAnd;
* Real maps: SUMO supports real maps (such as INTEGRATION and OsmAnd);
* Research employment: SUMO is the most frequent simulator in the field. To implement the vehicular traffic congestion avoidance solution, we injected a few subroutines in SUMO's route computation algorithm in order to compute the density, effort, speed and delay. The injected subroutines computation uses a series of parameters from the proposed thresholding mechanism. Another important aspect regarding the SUMO simulator is the fact that, in order to model vehicular traffic reality, it was necessary to add an average delay (8) value for each traffic light that is passed by a specific simulated vehicle. An efficient range of 8 values was determined by testing the topology of each road network. ### Evaluation Metrics and Scenarios

To address efficiency, usability and performance of the vehicular traffic thresholding mechanism used to avoid congestion, the following metrics were evaluated and discussed:

* \(s_{\text{uv}}\)--the average vehicle speed (km/h);
* \(d_{\text{total}}\)--the total distance travelled by all the running vehicles in one simulated scenario (km);
* \(t_{\text{total}}\)--the total travelled time of all the running vehicles in one simulated scenario (hours);
* \(t_{\text{simulation}}\)--the simulation time for all simulated vehicles in one simulated scenario (hours);
* \(fuel_{\text{total}}\)--the total fuel consumption for all vehicles in one simulated scenario (l);
* \(CO_{\text{2,total}}\)--the total \(CO_{\text{2}}\) emissions for all vehicles in one simulated scenario (kg);
* \(countV_{\text{peak}}\)--the peak number of concurrent vehicles running on roads in one simulated scenario. The running environment was a Windows 10 computer with a Ryzen 9 5900X 12-core processor and 32 GB of RAM and a Samsung EVO 980 PRO 2T SSD. The SUMO based simulation framework presented in the previous section runs on this machine in order to simulate vehicular traffic (especially congestion) that runs on a comprehensive set of urban road network topologies. For each simulated vehicle, the start and destination points of the corresponding route were randomly generated. The generated start and destination points belong to the selected urban area. For simplicity, we use the following notations in the rest of the article:

* SUMO--default SUMO solution used to simulate vehicles;

Figure 3: Vehicular traffic congestion avoidance implementation framework. * CASUMO\({}_{\delta}\)--our congestion avoidance solution integrated with default SUMO and considering that a vehicle is delayed on average by \(\delta\) seconds at each traffic light. It applies Algorithm 1 with its subroutines to a specific road network topology to generate vehicular routes that avoid congestion. During experiments, for each network topology, three \(\delta\) values were used. The evaluation of our thresholding mechanism for vehicular traffic congestion avoidance is defined based on scenarios driven by parameters corresponding to the above defined tuples:

* Number of Vehicles--representing the total number of vehicles during a given simulation:
* Free flow traffic that simulates vehicles in the range of [10 K+...30 K+];
* More than 50,000 (50 K+) vehicles during 2.8 h for moderate to high traffic congestion;
* More than 100,000 (100 K+) vehicles during 2.8 h for very high traffic congestion. * Road Network Topology--we simulated vehicles using different urban areas in order to cover the road network topologies presented in [21].