# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 5 Results and Discussion
### Gaussian Distribution Reward  
Figure 9 shows the results according to the Gaussian distribution reward. The results of 100 (A), 200 (B), 300 (C), and 400 (D) transportation types are displayed. Transportation times vary depending on the traffic density. Map distribution based on the distribution function and containing randomness reveals the results more accurately. In the Gaussian-distributed reward mechanism, when distribution multipliers are applied as rewards to eliminate the problems caused by randomness, the average rewards are very close in low-traffic environments and the bicycle is still the best transportation option. With increasing traffic density, although the average rewards are very close, the motorcycle shows a better spread and achieves a better result in average rewards. Figure 9 shows the results according to the Gaussian Distributed Reward mechanism. The results for the 100 (A), 200 (B), 300 (C), and 400 (D) transportation types are presented. Transportation times vary depending on low-traffic-density and high-traffic-density conditions. Regarding the mechanism, when distribution factors are applied as rewards to eliminate the problems caused by randomness, the average rewards are quite close to each other in low-traffic environments and the bicycle still stands out as the best transportation option. However, with the increase in traffic density, it is seen that although the average rewards are close to each other, the motorcycle shows a better distribution and achieves a better result in average rewards. In this context, when the statistics shown in Table 2 are examined in detail, it is clearly seen how the statistical differences, such as mean value and standard deviation, between vehicle types change with traffic density. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Mean** & **Maximum Value** & **Minimum Value** & **Standard Deviation** & **Median** & **Mode** \\ \hline  & \multicolumn{5}{c}{**Statistics of 300 vehicles and pedestrians (Total 1200)**} \\ \hline Car & 61.04 & 129.48 & 0.18 & 35.55 & 56.05 & 48.32 \\ Bicycle & 55.93 & 131.18 & 1.03 & 38.70 & 39.89 & 10.70 \\ Motorcycle & 60.34 & 130.27 & 0.43 & 38.08 & 53.70 & 15.08 \\ Pedestrian & 63.89 & 131.80 & 2.63 & 39.19 & 49.87 & 130.12 \\ \hline  & \multicolumn{5}{c}{**Statistics of 400 vehicles and pedestrians (Total 1600)**} \\ \hline Car & 75.96 & 240.51 & 0.27 & 45.69 & 79.08 & 37.67 \\ Bicycle & 76.34 & 159.93 & 1.03 & 43.51 & 74.20 & 17.38 \\ Motorcycle & 74.16 & 175.92 & 0.62 & 44.87 & 66.25 & 61.87 \\ Pedestrian & 87.42 & 168.05 & 2.97 & 47.80 & 76.95 & 34.42 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._For example, in the data of 100 vehicles and pedestrians (Total 400), bicycles and motorcycles have lower mean and standard deviation values. This indicates a more stable performance in a low-traffic density environment. In the data of 400 vehicles and pedestrians (Total 1600), it is seen that motorcycles show a better spread in average reward values and reach a higher standard deviation value. This supports the idea that motorcycles are a more advantageous option in heavy traffic environments. As a result, the performance differences of transportation types in both low- and high-traffic environments are supported by statistics, and it is seen that the Gaussian distribution reward mechanism reveals these differences fairly. Table 3 summarizes the statistics based on the Gaussian Distribution Model for vehicles and pedestrians at different traffic densities. Statistical measures such as mean, maximum and minimum, standard deviation, median, and mode values are presented for four different transportation modes (car, bicycle, motorcycle, and pedestrian) in each dataset size (100, 200, 300, and 400 vehicles and pedestrians). The table shows the changes in these measures with the increase in traffic density. At a low traffic density, reward values are generally lower and standard deviations are smaller, indicating a more stable performance.