# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 5 Results and Discussion  
This study assessed the efficiency of individual transport modes (walking, cycling, motorbiking, and driving) on a university campus scale. Public transport and high-capacity vehicles (e.g., minibuses and buses) were excluded from the analysis. The primary reason for this exclusion was to simplify the simulation structure and focus on analyzing the performance of individual transport modes under traffic congestion. Additionally, the study centers on real-world problems, and the absence of public transport vehicles such as buses and trams on the campus further justifies their exclusion. While it is well established that high-capacity vehicles are effective under high-density traffic conditions, their inclusion would require detailed route planning and demand forecasting models, which are beyond the scope of this study. More comprehensive analyses integrating these modes of transport are planned for future research. The arrival times given in the study are presented in Figure 7. There is no reward mechanism here. There are only total penalty times using the arrival time. It would be a useful way to find out which model is superior when no model is used, as well as which mode of transportation is more useful in various traffic scenarios. Figure 7 compares the arrival times of different transportation modes at different traffic densities. The horizontal line on the box in the graphs represents the median of the transportation times. These median values represent the middle value of the arrival times for each transportation mode when the starting points are randomly assigned. The figure shows that in low-density traffic scenarios (e.g., Figure 7A), the time it takes cyclists to reach their destination is generally shorter and the dispersion is narrower. However, as the traffic intensity increases  
Figure 6: A sample Poisson distribution generated in a large sample space. (e.g., Figure 7D), the median values converge and the distribution widens across transport modes. This clearly demonstrates the impact of factors such as high traffic density and traffic lights on the efficiency of transport modes. For example, Figure 7D shows that in a very dense traffic environment with 1600 vehicles and pedestrians, the automobile is more advantageous and the median arrival time is lower than other modes. Overall, the median values show how the performance in transport times varies even when the origin and destination points are randomized, highlighting the importance of transport mode choice for different traffic scenarios. The simulation ends when the transportation modes reach the target area after starting from random points. This evaluation is measured by the elapsed time. In order to make a better evaluation, an environment from low-density traffic to high-density traffic has been prepared. The average and general results for 100, 200, 300, and 400 vehicles are given in Figure 8. In low-density traffic environments, bicycle commuters' arrival times to their designated destination are significantly lower. However, as traffic density increases, the spread in target arrival times approaches. This is because in an environment with traffic lights and a high number of vehicles, transportation becomes extremely difficult. In the case of a very high density of vehicles, a total of 1600, it is seen that the best means of transportation is the car. Figure 7: Arrival time distribution by transportation mode. Each subgraph shows the simulation results with different numbers of vehicles and pedestrians: (**A**) 100 vehicles and pedestrians (400 in total), (**B**) 200 vehicles and pedestrians (800 in total), (**C**) 300 vehicles and pedestrians (1200 in total), and (**D**) 400 vehicles and pedestrians (1600 in total). The median and range of arrival times for each transportation mode are shown. As shown in Table 2, the mean is the average value representing the general trend of the data. The maximum value is the highest value encountered in the dataset.