# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 5 Results and Discussion
### Results Comparison with Baseline Models
As a result, the proposed framework offers wide potential for both small-scale (e.g., campuses) and large-scale (urban areas) applications. The performance of Gaussian and Poisson distribution models under traffic density proves the adaptability and flexibility of the proposed system to different conditions. This framework can make significant contributions to the development of urban transportation policies in the future. The proposed framework combines reinforcement learning algorithms and traffic simulations to provide a dynamic and adaptive approach to transportation mode selection. In terms of applicability, the framework is particularly suitable for traffic management and transport mode optimization in closed or semi-closed areas (e.g., university campuses or industrial zones). When tested at the campus scale, the model provided effective results by selecting the right transport modes under low- and medium-density traffic conditions. However, for the framework to be applied to large-scale urban areas, it needs to be extended to consider more complex traffic networks, user behavior, and environmental impacts. For example, the integration of real-time traffic data and the inclusion of public transportation systems in the framework will provide a wider potential for application. In terms of robustness, the proposed framework achieved a good performance in transportation mode selection in different traffic density scenarios. The Gaussian and Poisson reward mechanisms provided flexibility with regard to mode choice according to the traffic density and demonstrated the system's ability to adapt to unexpected traffic conditions. However, it was observed that the performance of the model may be limited in very high-density traffic environments. Therefore, in the future, additional enhancements are planned to improve the robustness of the model to various traffic anomalies (e.g., road closures and \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Category** & **B-c** & **RUM** & **RLRM** & **SM** & **GDM** & **PDM** \\ \hline Car (100) & 29.54 & 27.65 & 26.92 & 28.44 & 25.62 & 24.35 \\ Car (200) & 46.26 & 45.48 & 44.76 & 45.25 & 44.37 & 44.09 \\ Car (300) & 67.81 & 68.52 & 68.84 & 68.10 & 66.39 & 66.04 \\ Car (400) & 92.13 & 89.36 & 87.22 & 90.35 & 81.87 & 82.63 \\ \hline Bicycle (100) & 25.35 & 24.37 & 23.64 & 24.28 & 23.51 & 21.74 \\ Bicycle (200) & 39.80 & 38.94 & 37.84 & 38.51 & 36.80 & 37.09 \\ Bicycle (300) & 67.16 & 66.81 & 68.93 & 66.85 & 61.53 & 60.93 \\ Bicycle (400) & 85.41 & 83.27 & 84.14 & 84.48 & 82.41 & 83.01 \\ \hline Motorcycle (100) & 31.23 & 27.93 & 26.78 & 30.63 & 26.37 & 25.15 \\ Motorcycle (200) & 50.58 & 43.62 & 42.58 & 49.12 & 42.01 & 41.53 \\ Motorcycle (300) & 70.37 & 68.78 & 65.89 & 69.72 & 65.92 & 65.34 \\ Motorcycle (400) & 86.65 & 89.45 & 86.42 & 96.21 & 80.02 & 80.83 \\ \hline Pedestrian (100) & 36.10 & 26.41 & 27.83 & 35.38 & 25.62 & 24.35 \\ Pedestrian (200) & 56.83 & 43.71 & 42.32 & 55.54 & 44.37 & 44.09 \\ Pedestrian (300) & 79.37 & 69.62 & 66.24 & 78.65 & 66.39 & 66.04 \\ Pedestrian (400) & 92.29 & 95.83 & 94.62 & 98.14 & 81.87 & 82.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of mean travel times across different models and categories. accidents) and changes in user preferences. In conclusion, the proposed framework is applicable to both small-scale and large-scale applications. In this study, the performance of transport modes in different traffic density scenarios is analyzed. The results clearly show the advantages of each mode under different conditions:  
* Low Traffic Density : Cycling stands out as the most efficient mode of transportation at a low traffic density. This is mainly because bicycle users can move quickly and flexibly in low-density scenarios, independently of other vehicles. As they are less dependent on traffic lights, bicycle users generally prefer direct routes, significantly reducing total travel time. * Medium Traffic Density: In medium-traffic-density scenarios, the bicycle remains the most advantageous mode of transport.