# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility  
[PERSON]  
1 Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir 26040, Torkiye; [EMAIL_ADDRESS] (K.O.); [EMAIL_ADDRESS] (A.Y.)1 Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir 26040, Torkiye; [EMAIL_ADDRESS] (K.O.); [EMAIL_ADDRESS] (A.Y.)1 Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir 26040, Torkiye; kozkan@ogu.  
Transportation mode selection is an essential area regarding transportation systems. Different modes of transportation (car, motorcycle, bicycle, walking, etc.) are essential for mode selection and transportation characteristics, and mode choice analysis can solve traffic congestion or eliminate related transportation problems through considering different travel behaviors [5]. By using mode selection, it is possible to offer solutions that are suitable for different city conditions. By handling transportation mode selection differently, the best transportation method can be chosen, allowing for different alternatives to be used in situations where there is a lot of traffic.  
Mode selection methods are frequently used to solve problems such as travel and route planning. Mode selection aims to maximize the selection of vehicles available on a route [6]. Transportation modes such as walking, cycling, and driving each have their own parameters. These parameters include many factors, such as the specifics of the vehicle to be used (maximum speed, fuel consumption, etc.), the condition of the roads, and the effect of traffic lights on it [5]. The aim is to investigate how the relationships between travel time, costs, and land use patterns influence choice and travel patterns relevant to where people live and work. The urban form of housing, workplaces, and travel time and cost are crucial factors in travel choice [7], and transportation mode selection is bound to reduce many costs in urban life. Transportation cost, time, and shortest route can be considered as costs here. It is inevitable that these factors will be faced much more in bigger, real-world problems. The methods used in the study aim to address transportation time regarding the use of alternative modes of transportation during traffic congestion that may occur in an area during an event or mass organization.  
In addition to intuitive approaches, methods such as reinforcement learning (RL) and deep reinforcement learning (DRL) are also used to solve the transportation mode selection problem (TMSP). With these methods, it becomes possible to reach an instant solution by taking into account changing environmental activities. It is possible that different routes can provide more effective solutions due to instantaneous changes in the environment. Even in the conditions of roads or traffic lights used by vehicles or pedestrians, it is possible to obtain a better result using such approaches [8; 9].  
In this study, the following areas are addressed:  
* A unique reward mechanism model is presented using the simple model, Gaussian distribution, and Poisson distribution.
* Deliveries were made to a collection area on a university campus using different starting points and various transportation modes. Within the framework of this scenario, it was determined how the most efficient vehicle and the most appropriate route would be selected.
* To solve this problem, RL was used, and the best reward mechanism was determined with the help of MDP. How each vehicle traveled to its destination, the environmental restrictions, different variations, and the effect of these on the result were examined.
* SUMO, which is a simulation environment, was used to ensure the realization of the study and to control the steps.