# Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network
## 1 Introduction  
Traffic congestion is a common phenomenon that occurs when a signal is shorter than the number of vehicles attempting to pass. Owing to the increasing number of vehicles, traffic congestion is worsening and is one of the main problems that cities are trying to solve. In the era of the 4 th Industrial Revolution, research into smart cities using information communication and Internet of Things (IoT) technologies is being actively conducted. The urban management system of a smart city includes various fields, such as medical, transportation, safety, and energy. The platform that deals with traffic-related problems in smart cities is an intelligent transportation management system in which traffic flow is controlled to reduce traffic congestion. The current traffic signal control is a fixed signal control.  
In a traditional fixed signaling system, a green signal is assigned in the order in which it is input in advance. The system also distributes green signals of the same length, and the process is repeated even under various traffic conditions. However, traffic conditions dynamically and unexpectedly change over the time and spatial domains. There are times when more vehicles are moving, such as during rush-hour or the weekend, and times when relatively fewer vehicles are moving. As such, traffic signal systems have complex nonlinear stochastic systems because they are affected by various factors, such as traffic infrastructure and traffic load. Therefore, traffic signal systems have been previously studied for adaptive traffic signal control.  
Among the various studies on the traffic signal control system, there are studies using fuzzy technology [1, 2] as a type of CI technology, green wave technology [3], and traffic signal control using particle swarm optimization [4] as a heuristic technology. However,these systems require many resources, such as time or computing power, to calculate the optimal signaling strategy. Because of these resource constraints, the system can only be used on environments and rules that have already been calculated, and there is a limit to optimally control the traffic signal in a dynamic environment that changes in real time. The traffic control system has to make optimal decisions by adapting to the dynamic environment while recognizing the surrounding intersections. Therefore, research on controlling traffic signals using reinforcement learning (RL) has recently attracted attention [5,6].  
In this paper, we propose a traffic signal system using a deep Q-network (DQN), which is a type of reinforcement learning method. The proposed model recognizes traffic conditions at intersections and determines the phase order, that is, the order in which the green traffic light turns on. The purpose of the proposed model is to maximize the throughput of the intersection and distribute the signals fairly. Throughput refers to the number of vehicles passing through an intersection over time. The reason for the fair distribution of traffic signals is to avoid the risk of some vehicles waiting too long to increase the throughput.  
The remainder of this paper is organized as follows. Section 2 introduces related studies. Section 3 proposes a traffic signal control model using a DQN. Section 4 presents and discusses the experiment results. Finally, Section 5 provides some concluding remarks regarding this research.