Optimization of signal control at ramp adjacent intersections based on A2C
The operational efficiency of urban expressways significantly impacts citywide transportation flow. During morning and evening rush hours, the limited capacity of feeder roads to handle high traffic volumes leads to congestion on expressway exit ramps. This often results in queuing and, in severe ca...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Press (China Science Publishing & Media Ltd.)
2024-03-01
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| Series: | Shenzhen Daxue xuebao. Ligong ban |
| Subjects: | |
| Online Access: | https://journal.szu.edu.cn/en/#/digest?ArticleID=2606 |
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| Summary: | The operational efficiency of urban expressways significantly impacts citywide transportation flow. During morning and evening rush hours, the limited capacity of feeder roads to handle high traffic volumes leads to congestion on expressway exit ramps. This often results in queuing and, in severe cases, ramp spillback, causing traffic bottlenecks on the expressway lanes and substantial losses in traffic travel. This study utilizes deep reinforcement learning algorithms for traffic signal control optimization at exit ramps associated with intersection crossings. Traffic signals are treated as intelligent agents, and real-time traffic conditions of the expressway ramps and intersections are fed into the system using detectors. A dynamic reward function is introduced, adjusted based on the ratio of the remaining traffic capacity between the feeder roads and the exit ramps. The objective is to enhance ramp traffic efficiency while optimizing intersection signals. The methodology is applied to an expressway on East Third Ring Road, Beijing, and a related intersection, which utilizing the simulation of urban mobility (SUMO) traffic simulation platform and the Traci library to create a simulated environment. The results indicate that the signal control method, based on an improved advantage actor critic (A2C) algorithm, outperforms traditional signal controls and those based on the deep Q-network (DQN) algorithm. Especially during peak travel times, it effectively reduces the probability of ramp spillback and enhances the traffic efficiency of the interconnected feeder road intersections. |
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| ISSN: | 1000-2618 |