# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 5 Results and Discussion
### Poisson Distribution Reward  
Figure 10 shows the results according to the Poisson Distribution Reward. The results of 100 (A), 200 (B), 300 (C), and 400 (D) transportation types are displayed. Since the distribution formula gives sharper results, arriving at the destination on time is more rewarded, while arriving late is more penalized. For this reason, it has become preferable as a sharper reward mechanism. Compared to the Gaussian method, the Poisson Distribution Reward Mechanism is stricter. Cycling in low-density traffic appears to be generally preferable. When distribution multipliers are used as incentives in the setting where they are used to mitigate the problems introduced by randomness, the average rewards are quite similar at low-traffic locations, and cycling is still considered the best mode of transportation. As traffic density increases, the average rewards are very similar, but the motorcycle has a better dispersion and produces a superior average reward result. Where traffic congestion increases, average arrival times are very similar. Heavy traffic congestion has a significant impact on the time it takes each vehicle to reach its destination. Therefore, when making transportation mode selection, this is the representation of the mode that should be selected according to traffic congestion. In high-density traffic, the bicycle is the best mode, while in lower-density situations, the motorcycle is a better transportation mode. Figure 10 shows the results according to the Poisson distribution reward mechanism. While the bicycle stands out as the best option among the transportation modes at a low traffic density, it is seen that motorcycles performs better as traffic density increases. The sharp reward mechanism of the Poisson distribution rewards on-time arrivals more and penalizes late arrivals more. Therefore, a significant difference is observed between  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Mean** & **Maximum Value** & **Minimum Value** & **Standard Deviation** & **Median** & **Mode** \\ \hline \hline  & \multicolumn{6}{c}{**Statistics of 100 vehicles and pedestrians (Total 400)**} \\ \hline Car & 25.62 & 52.08 & 4.42 & 11.94 & 28.20 & 16.37 \\ Bicycle & 23.51 & 52.29 & 5.49 & 12.99 & 18.95 & 7.82 \\ Motorcycle & 26.37 & 52.16 & 4.31 & 14.32 & 28.38 & 7.52 \\ Pedestrian & 25.62 & 52.08 & 4.42 & 11.94 & 28.20 & 16.38 \\ \hline \hline  & \multicolumn{6}{c}{**Statistics of 200 vehicles and pedestrians (Total 800)**} \\ \hline Car & 44.37 & 86.41 & 4.87 & 23.67 & 43.65 & 81.47 \\ Bicycle & 36.80 & 86.89 & 6.73 & 22.12 & 28.76 & 21.91 \\ Motorcycle & 42.01 & 86.49 & 6.47 & 23.10 & 38.46 & 31.25 \\ Pedestrian & 44.37 & 86.41 & 4.87 & 23.67 & 43.65 & 81.48 \\ \hline \hline  & \multicolumn{6}{c}{**Statistics of 300 vehicles and pedestrians (Total 1200)**} \\ \hline Car & 66.39 & 131.46 & 3.17 & 35.61 & 57.89 & 49.32 \\ Bicycle & 61.53 & 133.00 & 4.12 & 37.84 & 48.58 & 15.16 \\ Motorcycle & 65.92 & 132.17 & 3.45 & 37.89 & 57.13 & 20.24 \\ Pedestrian & 66.39 & 131.46 & 3.17 & 35.60 & 57.89 & 49.32 \\ \hline \hline  & \multicolumn{6}{c}{**Statistics of 400 vehicles and pedestrians (Total 1600)**} \\ \hline Car & 81.87 & 240.53 & 2.26 & 45.89 & 87.86 & 43.53 \\ Bicycle & 82.41 & 161.69 & 3.08 & 43.35 & 82.95 & 20.94 \\ Motorcycle & 80.02 & 176.79 & 2.64 & 45.02 & 74.72 & 70.08 \\ Pedestrian & 81.87 & 240.53 & 2.26 & 45.89 & 87.86 & 43.53 \\ \hline \hline  & \multicolumn{6}{c}{**Statistics of 400 vehicles and pedestrians (Total 1600)**} \\ \hline Car & 81.87 & 240.53 & 2.26 & 45.89 & 87.86 & 43.53 \\ Bicycle & 82.41 & 161.69 & 3.08 & 43.35 & 82.95 & 20.94 \\ Motorcycle & 80.02 & 176.79 & 2.64 & 45.02 & 74.72 & 70.08 \\ Pedestrian & 81.87 & 240.53 & 2.26 & 45.89 & 87.86 & 43.  
transportation times as traffic density increases and the bicycle is considered a more suitable option in high-density traffic situations.