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Reinforcement Learning-based Traffic Signal Control under Real-World Constraints
Mingyu Pi, Hunsoon Lee, Moonyoung Chung
http://doi.org/10.5626/JOK.2021.48.8.871
Traffic signal control plays an important role in efficiently using the limited capacity of the road. Since traditional traffic signal control methods operate based on preset signals, it is difficult to cope with frequently changing traffic conditions. Recently, as reinforcement learning has attracted attention as a method for solving complex problems, studies using reinforcement learning for efficient traffic signal control are being conducted. Compared to the traditional method, it has been proved through simulation that waiting time and travel time were improved. However, since most of the studies did not reflect the limitations of the actual signal, it was designed inappropriately for practical application. In this paper, we proposed a signal control method based on reinforcement learning that could be applied to real situations by reflecting the constraints of the signal operating system that exist in reality, and that could respond to changes in traffic volume.
A Visual Analytics Technique for Analyzing the Cause and Influence of Traffic Congestion
Mingyu Pi, Hanbyul Yeon, Hyesook Son, Yun Jang
http://doi.org/10.5626/JOK.2020.47.2.195
In this paper, we present a technique to analyze the causes of traffic congestion based on the traffic flow theory. We extracted vehicle flows from the traffic data, such as GPS trajectory and Vehicle Detector data. Also, vehicle flow changes were identified by utilizing the entropy from the information theory. Then, we extracted cumulative vehicle count curves (N-curve) that can quantify the vehicle flows in the congestion area. According to the traffic flow theory, unique N-curve patterns can be observed depending on the congestion type. We build a convolution neural network classifier that can classify N-curve into four different congestion patterns. Analyzing the cause and influence of congestion is difficult and requires considerable experience and knowledge. Apparently, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of traffic congestion. Through case studies, we have evaluated our system that can analyze the cause of traffic congestion.
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