Research on the Application of Reinforcement Learning in Traffic Flow Prediction
As global urbanization accelerates, urban traffic issues are becoming increasingly severe. Traffic flow prediction (TFP), as a key technology in intelligent transportation systems, aims to provide decision support and optimization plans by analyzing vehicle flow, speed, and density in road networks....
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01005.pdf |
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| _version_ | 1825206546957598720 |
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| author | Hu Yiquan |
| author_facet | Hu Yiquan |
| author_sort | Hu Yiquan |
| collection | DOAJ |
| description | As global urbanization accelerates, urban traffic issues are becoming increasingly severe. Traffic flow prediction (TFP), as a key technology in intelligent transportation systems, aims to provide decision support and optimization plans by analyzing vehicle flow, speed, and density in road networks. However, traditional statistical models and prediction methods based on historical data exhibit many limitations when dealing with complex, dynamic, and nonlinear traffic flow data. The purpose of this paper is to discuss how Reinforcement Learning (RL) can be applied to TFP. RL optimizes strategies through interactions between agents and the environment to maximize cumulative rewards. High-dimensional state spaces and nonlinear problems can be handled with strong adaptability and strong adaptability. The paper provides a detailed review of the latest developments in Deep RL in the field of TFP, including the application of Q-learning and its variants in traffic signal control. Additionally, the article discusses the application of RL-based Long Short-Term Memory Networks, Graph Convolutional Networks (GCN), and Dynamic GCN in TFP. Although RL has achieved significant results in the field of TFP, its application still faces challenges such as data complexity, dynamics, and high computational resource consumption. The paper suggests that future research directions should include expanding abnormal data, improving model efficiency and scalability, and extending application scenarios to further advance intelligent transportation systems. |
| format | Article |
| id | doaj-art-1fdc2ba0d12d4c7d9d9279eb3e6c54c0 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-1fdc2ba0d12d4c7d9d9279eb3e6c54c02025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700100510.1051/itmconf/20257001005itmconf_dai2024_01005Research on the Application of Reinforcement Learning in Traffic Flow PredictionHu Yiquan0College of Intelligent and Computing, Tianjin UniversityAs global urbanization accelerates, urban traffic issues are becoming increasingly severe. Traffic flow prediction (TFP), as a key technology in intelligent transportation systems, aims to provide decision support and optimization plans by analyzing vehicle flow, speed, and density in road networks. However, traditional statistical models and prediction methods based on historical data exhibit many limitations when dealing with complex, dynamic, and nonlinear traffic flow data. The purpose of this paper is to discuss how Reinforcement Learning (RL) can be applied to TFP. RL optimizes strategies through interactions between agents and the environment to maximize cumulative rewards. High-dimensional state spaces and nonlinear problems can be handled with strong adaptability and strong adaptability. The paper provides a detailed review of the latest developments in Deep RL in the field of TFP, including the application of Q-learning and its variants in traffic signal control. Additionally, the article discusses the application of RL-based Long Short-Term Memory Networks, Graph Convolutional Networks (GCN), and Dynamic GCN in TFP. Although RL has achieved significant results in the field of TFP, its application still faces challenges such as data complexity, dynamics, and high computational resource consumption. The paper suggests that future research directions should include expanding abnormal data, improving model efficiency and scalability, and extending application scenarios to further advance intelligent transportation systems.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01005.pdf |
| spellingShingle | Hu Yiquan Research on the Application of Reinforcement Learning in Traffic Flow Prediction ITM Web of Conferences |
| title | Research on the Application of Reinforcement Learning in Traffic Flow Prediction |
| title_full | Research on the Application of Reinforcement Learning in Traffic Flow Prediction |
| title_fullStr | Research on the Application of Reinforcement Learning in Traffic Flow Prediction |
| title_full_unstemmed | Research on the Application of Reinforcement Learning in Traffic Flow Prediction |
| title_short | Research on the Application of Reinforcement Learning in Traffic Flow Prediction |
| title_sort | research on the application of reinforcement learning in traffic flow prediction |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01005.pdf |
| work_keys_str_mv | AT huyiquan researchontheapplicationofreinforcementlearningintrafficflowprediction |