# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 3 Vehicular Traffic Congestion Avoidance Methodology
\begin{table}
\begin{tabular}{r r r r} \hline \hline
**Metric** & **CASUMO\({}_{\mathbf{4}}\)** & **CASUMO\({}_{\mathbf{5}}\)** & **CASUMO\({}_{\mathbf{6}}\)** \\ \hline s\({}_{\text{av}}\) & 116.16 & 121.05 & 125.30 \\ d\({}_{\text{total}}\) & \(-\)23.50 & \(-\)28.62 & \(-\)34.45 \\ t\({}_{\text{total}}\) & 42.87 & 41.81 & 40.32 \\ t\({}_{\text{simulation}}\) & 17.55 & 16.98 & 20.48 \\ fuel\({}_{\text{total}}\) & 39.36 & 37.72 & 35.89 \\ CO\({}_{\text{2-total}}\) & 39.34 & 37.69 & 35.86 \\ countV\({}_{\text{peak}}\) & 39.76 & 40.00 & 37.66 \\ \hline \end{tabular}
\end{table} 
Table 7: Percentage improvement of the proposed solution 50 K+ simulated vehicles in Bucharest. \begin{table}
\begin{tabular}{r r r r r} \hline \hline
**Metric** & **SUMO** & **CASUMO\({}_{\mathbf{7}}\)** & **CASUMO\({}_{\mathbf{8}}\)** & **CASUMO\({}_{\mathbf{9}}\)** \\ \hline s\({}_{\text{av}}\) & 4.16 & 17.04 & 17.55 & 17.78 \\ d\({}_{\text{total}}\) & 264,339.49 & 333,266.25 & 338,454.75 & 343,217.67 \\ t\({}_{\text{total}}\) & 63,536.53 & 19,556.52 & 19,287.23 & 19,301.42 \\ t\({}_{\text{simulation}}\) & 9.13 & 5.48 & 5.26 & 5.41 \\ fuel\({}_{\text{total}}\) & 215,887.96 & 83,254.05 & 82,873.17 & 83,220.06 \\ CO\({}_{\text{2-total}}\) & 501,665.13 & 193,577.42 & 192,694.59 & 193,503.31 \\ countV\({}_{\text{peak}}\) & 20,008 & 7767 & 7666 & 7681 \\ \hline \end{tabular}
\end{table} 
Table 8: Results for 50 K+ simulated vehicles in Tokyo. Figure 7: Speed distribution in clusters for 50 K+ simulated vehicles in Bucharest with \(\delta=4\). ### Scenarios with 100 K+ Simulated Vehicles

The \(\delta\) value used to obtain the best \(t_{total}\) improvement value for the 50 K+ scenarios was also used to simulate high congestion by running 100 K+ vehicles in the same four urban areas. For each urban region, a table that contains the absolute value for all the above defined metrics is presented in addition to the percentage improvement in comparison with SUMO. In addition, a chart with speed distribution in clusters is presented for each scenario. #### 4.4.1 Grid Topology--Barcelona

In the case of high congestion, a grid topology that follows some rules can still support an improvement of more than 32% for the total time spent in traffic, whereas the average vehicle speed improvement reaches more than 65% (see Table 10). As shown in Figure 9, because the congestion level is high, approximately 47% of the vehicles have speeds below 10 km/h, whereas only approximately 17.6% of the vehicles have the speeds below 5 km/h. Figure 8: Speed distribution in clusters for 50 K+ simulated vehicles in Tokyo with \(\delta\) = 8. \begin{table}
\begin{tabular}{r r r r} \hline \hline
**Metric** & **CASUMO\({}_{7}\)** & **CASUMO\({}_{8}\)** & **CASUMO\({}_{9}\)** \\ \hline s\({}_{\text{av}}\) & 309.60 & 321.79 & 327.41 \\ d\({}_{\text{total}}\) & \(-\)26.08 & \(-\)28.04 & \(-\)29.84 \\ t\({}_{\text{total}}\) & 69.22 & 69.64 & 69.62 \\ t\({}_{\text{simulation}}\) & 40.00 & 42.46 & 40.82 \\ fuel\({}_{\text{total}}\) & 61.44 & 61.61 & 61.45 \\ CO\({}_{\text{2-net}}\) & 61.41 & 61.59 & 61.43 \\ countV\({}_{\text{peak}}\) & 61.18 & 61.69 & 61.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Percentage improvement of the proposed solution for 50 K+ simulated vehicles in Tokyo. #### 4.4.2 Unbalanced Grid Topology--New York

The high congestion scenario in New York shows that, in addition to grid topology, it is worth having a constant distribution of the grids. As can be observed from Table 11, because of the unbalanced distribution, the amount of time in traffic is decreased only with 9%, whereas the speed has a 37% improvement. The unbalanced distribution of the grid topology also impacts the vehicle speeds, as shown in Figure 10: approximately 75% of the simulated vehicles have speeds below 10 km/h and 36% of the simulated vehicles have speeds below 5 km/h.