Abstract
Traffic congestion is a worsening problem owing to an increase in traffic volume. Traffic congestion increases the driving time and wastes fuel, generating large amounts of fumes and accelerating environmental pollution. Therefore, traffic congestion is an important problem that needs to be addressed. Smart transportation systems manage various traffic problems by utilizing the infrastructure and networks available in smart cities. The traffic signal control system used in smart transportation analyzes and controls traffic flow in real time. Thus, traffic congestion can be effectively alleviated. We conducted preliminary experiments to analyze the effects of throughput, queue length, and waiting time on the system performance according to the signal allocation techniques. Based on the results of the preliminary experiment, the standard deviation of the queue length is interpreted as an important factor in an order allocation technique. A smart traffic signal control system using a deep Q-network, which is a type of reinforcement learning, is proposed. The proposed algorithm determines the optimal order of a green signal. The goal of the proposed algorithm is to maximize the throughput and efficiently distribute the signals by considering the throughput and standard deviation of the queue length as reward parameters.
Highlights
Traffic congestion is a common phenomenon that occurs when a signal is shorter than the number of vehicles attempting to pass
We propose a traffic signal system using a deep Q-network (DQN), which is a type of reinforcement learning method
QT-CDQN applied the average queue length, CRL applied on the sum of queue lengths, and tp + qlstd applied the standard deviation of the queue length
Summary
Traffic congestion is a common phenomenon that occurs when a signal is shorter than the number of vehicles attempting to pass. 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. These systems require many resources, such as time or computing power, to calculate the optimal signaling strategy.
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