# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 3 Vehicular Traffic Congestion Avoidance Methodology
These are the orange zones shown in Figure 2.0
* Grid topology: Barcelona--almost ideal urban area with dense road topology in the form of a grid, surrounded by highways and a proportional surface;
* Historical/Irregular Topology: Bucharest--urban areas with a small density of road infrastructure following random geometry based on historical roads and proportional surface;
* Unbalanced Grid Topology: New York--urban area with long roads, highways, grid topology, disproportional surface and pedestrian subareas;
* Hybrid Topology: Tokyo--urban area with very rich road infrastructure that has many highways, large intersections and a proportional surface. * \(\delta\)--average time (in seconds) spent by each vehicle at a traffic light during simulation. This is a natural number that is in the range of [1...9] seconds and was experimentally identified for each road network topology. ### Scenarios with 50 K+ Simulated Vehicles

For vehicular scenarios with 50 K+ simulated vehicles (moderate vehicular traffic congestion), we run one test with SUMO and three tests with \(\delta\) for each urban area. The \(\delta\) values were selected to reduce \(t_{total}\) as much as possible. Two other important metrics that evolve with \(t_{total}\) are \(fuel_{total}\) and CO\({}_{2}\) emissions (\(CO_{2_{total}}\)). It is interesting to observe the \(s_{av}\) evolution that depends on \(t_{total}\) and \(d_{total}\) because, in most of the cases, \(d_{total}\) is inversely with \(t_{total}\) and, therefore, \(s_{av}\) improvement is very high. For each scenario that runs 50 K+ vehicles in the selected urban regions, we present the following:

* A table with absolute values obtained for the above defined metrics;
* A table with improvement values (in percentage) obtained by our solution in comparison with SUMO;
* A chart with speed distribution in clusters is presented for the \(\delta\) value that corresponds to the minimum value of \(t_{total}\). #### 4.3.1 Grid Topology--Barcelona

As can be observed from Tables 2 and 3, for moderate congestion in Barcelona, we obtained more than a 37% improvement for the time spent in traffic, while the improvement of average speed was more than 77%. Figure 5 shows that the traffic has a good flow because most of the vehicles' speeds are above 10 km/h. \begin{table}
\begin{tabular}{r r r r r} \hline \hline
**Metric** & **SUMO** & **CASUMO1** & **CASUMO2** & **CASUMO3** \\ \hline s\({}_{\text{av}}\) & 10.90 & 19.29 & 19.78 & 19.94 \\ d\({}_{\text{total}}\) & 320,524.76 & 353,702.60 & 372,673.01 & 384,579.08 \\ t\({}_{\text{total}}\) & 29,415.87 & 18,333.38 & 18,841.57 & 19,291.14 \\ t\({}_{\text{simulation}}\) & 6.29 & 4.91 & 4.93 & 4.76 \\ fuel\({}_{\text{total}}\) & 97,945.29 & 63,614.5 & 65,148.6 & 66,319.91 \\ CO\({}_{\text{2,total}}\) & 227,660.31 & 147,909.91 & 151,479.26 & 154,204.95 \\ countV\({}_{\text{peak}}\) & 11,560 & 7242 & 7494 & 7693 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for 50 K+ simulated vehicles in Barcelona. Figure 4: Maps of simulation areas. Orange shaded the highly congested area (65 km\({}^{2}\)). #### 4.3.2 Unbalanced Grid Topology--New York

As shown in Tables 4 and 5, the moderate congestion scenario in New York benefits from more than a 39% improvement for the total time spent in traffic, and the average vehicle speed is almost doubled (\(\geq\)88%). The chart in Figure 6 shows that the traffic has a good flow because approximately 80% of the vehicles' speeds are above 10 km/h.