# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 3 Vehicular Traffic Congestion Avoidance Methodology
\begin{table}
\begin{tabular}{r r r r r} \hline \hline
**Metric** & **SUMO** & **CASUMO\({}_{\mathbf{4}}\)** & **CASUMO\({}_{\mathbf{5}}\)** & **CASUMO\({}_{\mathbf{6}}\)** \\ \hline s\({}_{\text{av}}\) & 8.10 & 15.26 & 15.65 & 15.68 \\ d\({}_{\text{total}}\) & 377,724.01 & 427,372.24 & 434,299.56 & 442,962.90 \\ t\({}_{\text{total}}\) & 46,604.69 & 28,004.75 & 27,753.99 & 28,249.23 \\ t\({}_{\text{simulation}}\) & 7.68 & 7.08 & 6.93 & 7.05 \\ fuel\({}_{\text{total}}\) & 170,255.79 & 112,553.47 & 112,128.85 & 113,662.42 \\ CO\({}_{\text{2-real}}\) & 395,695.54 & 261,666.28 & 260,681.83 & 264,247.54 \\ countV\({}_{\text{peak}}\) & 15,830 & 11,177 & 11,189 & 11,237 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for 50 K+ simulated vehicles in New York. Figure 5: Speed distribution in clusters for 50 K+ simulated vehicles in Barcelona with \(\delta=1\). \begin{table}
\begin{tabular}{r r r r} \hline \hline
**Metric** & **CASUMO\({}_{\mathbf{1}}\)** & **CASUMO\({}_{\mathbf{2}}\)** & **CASUMO\({}_{\mathbf{3}}\)** \\ \hline s\({}_{\text{av}}\) & 77.06 & 81.52 & 82.96 \\ d\({}_{\text{total}}\) & \(-\)10.35 & \(-\)16.27 & \(-\)19.98 \\ t\({}_{\text{total}}\) & 37.68 & 35.95 & 34.42 \\ t\({}_{\text{simulation}}\) & 21.93 & 21.55 & 24.37 \\ fuel\({}_{\text{total}}\) & 35.05 & 33.48 & 32.29 \\ CO\({}_{\text{2-real}}\) & 35.03 & 33.46 & 32.27 \\ countV\({}_{\text{peak}}\) & 37.35 & 35.17 & 33.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage Improvement of the proposed solution for 50 K+ simulated vehicles in Barcelona. #### 4.3.3 Historical/Irregular Topology

Bucharest

In the case of moderate congestion on a historical road network topology, the total time spent in traffic improvement by our solution was more than 42%, while the average vehicle speed was more than double that of SUMO (Tables 6 and 7). As is shown in Figure 7, more than 85% of the vehicles' speeds were at least 10 km/h. \begin{table}
\begin{tabular}{r r r r r} \hline \hline
**Metric** & **SUMO** & **CASUMO\({}_{4}\)** & **CASUMO\({}_{5}\)** & **CASUMO\({}_{6}\)** \\ \hline s\({}_{\text{av}}\) & 8.02 & 17.33 & 17.72 & 18.06 \\ d\({}_{\text{total}}\) & 247,890.68 & 306,138.41 & 318,841.39 & 333,290.65 \\ t\({}_{\text{total}}\) & 30,921.99 & 17,666.27 & 17,992.65 & 18,452.92 \\ t\({}_{\text{simulation}}\) & 5.23 & 4.31 & 4.34 & 4.16 \\ fuel\({}_{\text{total}}\) & 104,683.61 & 63,475.44 & 65,198.86 & 67,108.47 \\ CO\({}_{2_{\text{total}}}\) & 243,287.52 & 147,586.32 & 151,595.38 & 156,038.63 \\ countV\({}_{\text{peak}}\) & 12,556 & 7564 & 7534 & 7828 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for 50 K+ simulated vehicles in Bucharest. \begin{table}
\begin{tabular}{r r r r} \hline \hline
**Metric** & **CASUMO\({}_{4}\)** & **CASUMO\({}_{5}\)** & **CASUMO\({}_{6}\)** \\ \hline s\({}_{\text{av}}\) & 88.29 & 93.07 & 93.47 \\ d\({}_{\text{total}}\) & \(-\)13.14 & \(-\)14.98 & \(-\)17.27 \\ t\({}_{\text{total}}\) & 39.91 & 40.45 & 39.39 \\ t\({}_{\text{simulation}}\) & 7.78 & 9.76 & 8.13 \\ fuel\({}_{\text{total}}\) & 33.89 & 34.14 & 33.24 \\ CO\({}_{2_{\text{total}}}\) & 33.87 & 34.12 & 33.22 \\ countV\({}_{\text{peak}}\) & 29.39 & 29.32 & 29.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage improvement of the proposed solution for 50 K+ simulated vehicles in New York. Figure 6: Speed distribution in clusters for 50 K+ simulated vehicles in New York with \(\delta=5\). #### 4.3.4 Hybrid Topology--Tokyo

The data in Tables 8 and 9 shows that a hybrid and rich topology, such as the road network in Tokyo, is preferred by our solution, which reduces the total time spent in traffic by more than 69%, whereas the average vehicle speed is more than four times faster than the average vehicles speed in SUMO. In this case, as shown in Figure 8, more than 90% of the vehicles had speeds above 10 km/h.