# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 5 Results and Discussion
### Results Comparison with Baseline Models  
In this study, the performance of the proposed framework on different transportation modes is evaluated by comparing the simple model (SM), Gaussian distribution model (GDM), Poisson distribution model (PDM), and three different baseline models--B-coefficient (B-c), Random Utility Model (RUM), and Reinforcement Learning Reward Model (RLRM). Table 5 presents the average travel times of these models under different traffic density scenarios. According to the table, each model and transportation mode exhibited different performances under certain conditions, which revealed the importance of optimizing model selection according to traffic density and transportation mode. The results presented in Table 5 clearly show that the Gaussian (GDM) and Poisson (PDM) distribution models are superior to other models in terms of performance. GDM and PDM provided lower travel times than the simple model (SM), especially in low- and medium-density traffic scenarios, and their superiority was maintained at different traffic densities. At a high traffic density, it was observed that GDM and PDM produce optimized results in this mode thanks to the maneuverability and speed advantage of motorcycles. Bicycles are the most efficient transportation mode at low and medium traffic densities. They reduce the total travel time, with less dependence on traffic lights and flexible mobility. The GDM and PDM optimize the performance of bicycles under these conditions. In high-density traffic conditions, motorcycles have a superior performance compared to other modes. The GDM and PDM manage to keep the total travel time at the lowest level for this mode and flexibly adapt to the traffic density. Although the pedestrian mode is an effective option for short distances, it has a lower performance compared to other modes for long distances \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Mean** & **Maximum Value** & **Minimum Value** & **Standard Deviation** & **Median** & **Mode** \\ \hline  & \multicolumn{6}{c}{**Statistics of 100 vehicles and pedestrians (Total 400)**} \\ \hline Car & 24.35 & 45.02 & 0.22 & 12.34 & 29.88 & 0.22 \\ Bicycle & 21.74 & 46.57 & 1.06 & 12.63 & 20.94 & 1.06 \\ Motorcycle & 25.15 & 45.78 & 0.67 & 14.28 & 30.08 & 0.67 \\ Pedestrian & 24.35 & 45.01 & 0.22 & 12.34 & 29.88 & 0.22 \\ \hline  & \multicolumn{6}{c}{**Statistics of 200 vehicles and pedestrians (Total 800)**} \\ \hline Car & 44.09 & 84.05 & 0.21 & 24.27 & 51.37 & 0.21 \\ Bicycle & 37.09 & 84.65 & 1.61 & 21.76 & 36.62 & 1.61 \\ Motorcycle & 41.53 & 84.15 & 1.41 & 23.82 & 46.17 & 1.41 \\ Pedestrian & 44.09 & 84.05 & 0.21 & 24.27 & 51.37 & 0.21 \\ \hline  & \multicolumn{6}{c}{**Statistics of 300 vehicles and pedestrians (Total 1200)**} \\ \hline Car & 66.04 & 129.54 & 0.20 & 35.77 & 70.13 & 0.20 \\ Bicycle & 60.93 & 131.24 & 1.05 & 37.67 & 53.98 & 1.05 \\ Motorcycle & 65.34 & 130.33 & 0.45 & 38.14 & 67.78 & 0.45 \\ Pedestrian & 66.04 & 129.54 & 0.20 & 35.77 & 70.14 & 0.20 \\ \hline  & \multicolumn{6}{c}{**Statistics of 400 vehicles and pedestrians (Total 1600)**} \\ \hline Car & 82.63 & 240.61 & 0.28 & 46.10 & 97.83 & 0.29 \\ Bicycle & 83.01 & 160.03 & 1.06 & 43.39 & 92.91 & 1.06 \\ Motorcycle & 80.83 & 176.00 & 0.64 & 44.93 & 85.00 & 0.64 \\ Pedestrian & 82.63 & 240.61 & 0.29 & 46.11 & 97.83 & 0.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Vehicle and pedestrian statistics across different dataset sizes for the Poisson distribution model. and at a high traffic density. However, the GDM and PDM are effective in optimizing pedestrian performance. We have examined the performance of transportation modes under different traffic densities in detail and demonstrated the consistency of the GDM and PDM in different traffic conditions. In the future, we plan to expand the model further with real-time data integration, public transportation systems, and cost analyses. In addition, evaluation of environmental impacts and carbon emissions will enable the model to contribute to sustainable transportation policies.