Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 2
430-434

Attention mechanism based deep reinforcement learning for traffic signal control

Ren Anni1
Zhou Dake1
Feng Jinhao2
Tang Muyao1
Li Tao1
1. School of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 211100, China
2. School of Information Science & Engineering, Northeastern University, Shenyang 110004, China
DOI: 10.19734/j.issn.1001-3695.2022.06.0334

Abstract

DRL has gained wild applications in the field of urban transportation signal control. However, the existing DRL traffic signal control researches only use the traditional deep neural network, and its perception ability is limited in complex traffic scenarios. In addition, as one of the three elements of reinforcement learning, it also needs to design the traffic state carefully and manually in the existing researches. Therefore, this paper proposed a DRL traffic signal control algorithm based on attention mechanism. By introducing the attention mechanism, the neural network could automatically pay attention to the important state components to enhance the perception ability of the network, improve the signal control effect, and reduce the difficulty of state vector design. Experimental results on SUMO platform show that compared with the three classical signal control algorithms, only using a simple traffic state, the proposed algorithm has the best performance in average waiting time and travel time under the condition of low and high traffic flow at single intersections and multiple intersections.

Foundation Support

国家自然科学基金资助项目(62073164)
南京航空航天大学研究生创新基地(实验室)开放基金资助项目(xcxjh20210319)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0334
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: Algorithm Research & Explore
Pages: 430-434
Serial Number: 1001-3695(2023)02-019-0430-05

Publish History

[2022-09-20] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

任安妮, 周大可, 冯锦浩, 等. 基于注意力机制的深度强化学习交通信号控制 [J]. 计算机应用研究, 2023, 40 (2): 430-434. (Ren Anni, Zhou Dake, Feng Jinhao, et al. Attention mechanism based deep reinforcement learning for traffic signal control [J]. Application Research of Computers, 2023, 40 (2): 430-434. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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