Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement Learning
With the rapid growth of urban populations,the number of private cars has grown exponentially,which makes overwhelming traffic congestion problem become more and more acute.The traditional traffic signal control technology is difficult to adapt to the complex and changeable traffic conditions,and th...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2023-04-01
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| Series: | Jisuanji kexue |
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| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-159.pdf |
| _version_ | 1797845039747956736 |
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| author | YU Ze, NING Nianwen, ZHENG Yanliu, LYU Yining, LIU Fuqiang, ZHOU Yi |
| author_facet | YU Ze, NING Nianwen, ZHENG Yanliu, LYU Yining, LIU Fuqiang, ZHOU Yi |
| author_sort | YU Ze, NING Nianwen, ZHENG Yanliu, LYU Yining, LIU Fuqiang, ZHOU Yi |
| collection | DOAJ |
| description | With the rapid growth of urban populations,the number of private cars has grown exponentially,which makes overwhelming traffic congestion problem become more and more acute.The traditional traffic signal control technology is difficult to adapt to the complex and changeable traffic conditions,and the data-driven methods bring new research directions for the control-based system.The combination of deep reinforcement learning and traffic control systems plays an important role in adaptive traffic signal control.First,this paper reviews the latest progress in the application of intelligent traffic signal control systems,the methods of intelligent traffic signal control are classified and discussed,and the existing works in this field are summarized.The deep reinforcement learning method can effectively solve the problems of inaccurate state information acquisition,poor algorithm robust and weak regional coordination control ability in intelligent traffic signal control.Then,on the basis of the above,this paper gives an overview of the simulation platforms and experimental setup for intelligent traffic signal control,and analyzes and verifies it through examples.Finally,The challenges and unsolved problems in this field are discussed and future research directions are summarized. |
| first_indexed | 2024-04-09T17:32:06Z |
| format | Article |
| id | doaj.art-06734df53d6f448da9adb73137c51667 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-04-09T17:32:06Z |
| publishDate | 2023-04-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-06734df53d6f448da9adb73137c516672023-04-18T02:33:33ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2023-04-0150415917110.11896/jsjkx.220500261Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement LearningYU Ze, NING Nianwen, ZHENG Yanliu, LYU Yining, LIU Fuqiang, ZHOU Yi01 School of Artificial Intelligence,Henan University,Zhengzhou 450046,China ;2 College of Computer Science and Electronic Engineering,Hunan University,Changsha 410006,China ;3 College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China ;4 Shenzhen Research Institute of Henan University,Shenzhen,Guangdong 518000,ChinaWith the rapid growth of urban populations,the number of private cars has grown exponentially,which makes overwhelming traffic congestion problem become more and more acute.The traditional traffic signal control technology is difficult to adapt to the complex and changeable traffic conditions,and the data-driven methods bring new research directions for the control-based system.The combination of deep reinforcement learning and traffic control systems plays an important role in adaptive traffic signal control.First,this paper reviews the latest progress in the application of intelligent traffic signal control systems,the methods of intelligent traffic signal control are classified and discussed,and the existing works in this field are summarized.The deep reinforcement learning method can effectively solve the problems of inaccurate state information acquisition,poor algorithm robust and weak regional coordination control ability in intelligent traffic signal control.Then,on the basis of the above,this paper gives an overview of the simulation platforms and experimental setup for intelligent traffic signal control,and analyzes and verifies it through examples.Finally,The challenges and unsolved problems in this field are discussed and future research directions are summarized.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-159.pdfintelligent transportation system|deep reinforcement learning|traffic signal control|multi-agent |
| spellingShingle | YU Ze, NING Nianwen, ZHENG Yanliu, LYU Yining, LIU Fuqiang, ZHOU Yi Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement Learning Jisuanji kexue intelligent transportation system|deep reinforcement learning|traffic signal control|multi-agent |
| title | Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement Learning |
| title_full | Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement Learning |
| title_fullStr | Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement Learning |
| title_full_unstemmed | Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement Learning |
| title_short | Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement Learning |
| title_sort | review of intelligent traffic signal control strategies driven by deep reinforcement learning |
| topic | intelligent transportation system|deep reinforcement learning|traffic signal control|multi-agent |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-159.pdf |
| work_keys_str_mv | AT yuzeningnianwenzhengyanliulyuyiningliufuqiangzhouyi reviewofintelligenttrafficsignalcontrolstrategiesdrivenbydeepreinforcementlearning |