# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 6 Conclusions  
This paper focuses on transportation mode selection. Particularly, issues with traffic congestion and employing the right type of transportation considering the specific environment are examined. The study uses RL as a solution for transportation mode selection. In this context, various approaches were tested by creating different reward models. These reward models included approaches such as simple reward, Gaussian Distribution Reward, and Poisson Distribution Reward. The study results show that traffic density is vital in transportation mode selection. Research has been carried out by creating various reward models to determine which mode of transportation should be preferred, and the results show that traffic density, in particular, determines which mode of transportation is preferred. In addition, travel time was highlighted as important in the decision-making mechanism. A mathematical basis was established by stating that starting early does not make a difference in terms of unnecessary waiting but does in terms of different traffic densities. This basis includes a target time as the average of the arrival times of all vehicles to make the mode choice usable in different scenarios and environments. While a bicycle is a very good means of transportation in low-density traffic, a motorcycle is a better option in heavy traffic. In conclusion, this study shows that a reinforcement learning-basedapproach to transportation mode selection can provide an effective solution, considering traffic density and travel time factors. Such models are expected to positively impact future urban planning and transportation strategies. The Gaussian and Poisson reward mechanisms used in the study were chosen depending on the traffic density and dispersion characteristics. The Gaussian model is used to ensure a balanced distribution of rewards at low and medium traffic volumes. This model allows users to be rewarded more consistently in terms of travel time and speed when traffic conditions are more stable. The Poisson model is more suitable for high-density traffic. This model provides a more sensitive solution to extreme traffic conditions, such as congestion, by creating sharper and denser reward/penalty structures. This study proposes a methodology for transportation mode selection at different traffic densities using reinforcement learning algorithms and traffic simulation. The obtained results highlight the effectiveness of transport modes such as bicycles and motorcycles, especially in low- and high-traffic-density scenarios. However, this study was carried out on a limited campus scale and scalability to real-world scenarios has some limitations. Real-world applications should be extended to include more complex traffic networks, user preferences, and environmental factors. For example, different city structures, larger-scale traffic networks, and the integration of public transportation modes will require an extended version of the method. Furthermore, factors such as the compatibility of the proposed model with real-time data and computational cost in large-scale simulations need to be evaluated. In the future, real-world data will be used to improve the accuracy of the model and test its applicability for large-scale scenarios. This will make the proposed framework applicable not only at the campus scale but also to traffic management problems in large cities. In future studies, parameters that negatively affect traffic, such as waiting times at traffic lights, will also be added to the model, as well as many other variables such as possible alternative routes (instead of entering an area with a red light). Traffic density can be generated by adding more diverse distribution functions and more vehicle types. In addition, there will be a focus on integrating public transport options (e.g., buses and minibuses) into the proposed framework. This expansion will require the development of specific reward mechanisms to assess the performance of these modes in different traffic densities and scenarios.