A reinforcement learning approach for reducing traffic congestion using deep Q learning
Abstract Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown dramatically, while road and transportation infrastructure capacities have yet to expand proportionally to handle the additional traffic effectively. Road congestion and traffic-related pollution ha...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-75638-0 |
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| author | S M Masfequier Rahman Swapno SM Nuruzzaman Nobel Preeti Meena V. P. Meena Ahmad Taher Azar Zeeshan Haider Mohamed Tounsi |
| author_facet | S M Masfequier Rahman Swapno SM Nuruzzaman Nobel Preeti Meena V. P. Meena Ahmad Taher Azar Zeeshan Haider Mohamed Tounsi |
| author_sort | S M Masfequier Rahman Swapno |
| collection | DOAJ |
| description | Abstract Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown dramatically, while road and transportation infrastructure capacities have yet to expand proportionally to handle the additional traffic effectively. Road congestion and traffic-related pollution have increased, which is detrimental to society and public health. This paper proposes a novel reinforcement learning (RL)-based method to reduce traffic congestion. We have developed a sophisticated Deep Q-Network (DQN) and integrated it smoothly into our system. In this study, Our implemented DQL model reduced queue lengths by 49% and increased incentives for each lane by 9%. The results emphasize the effectiveness of our method in setting strong traffic reduction standards. This study shows that RL has excellent potential to improve both transport efficiency and sustainability in metropolitan areas. Moreover, utilizing RL can significantly improve the standards for reducing traffic and easing urban traffic congestion. |
| format | Article |
| id | doaj-art-52d9fe62acf34f8e9acae05e9e297d28 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-52d9fe62acf34f8e9acae05e9e297d282024-12-15T12:08:03ZengNature PortfolioScientific Reports2045-23222024-12-0114112010.1038/s41598-024-75638-0A reinforcement learning approach for reducing traffic congestion using deep Q learningS M Masfequier Rahman Swapno0SM Nuruzzaman Nobel1Preeti Meena2V. P. Meena3Ahmad Taher Azar4Zeeshan Haider5Mohamed Tounsi6Department of CSE, Bangladesh University of Business and TechnologyDepartment of CSE, Bangladesh University of Business and TechnologyDepartment of Electrical Engineering, Indian Institute of TechnologyDepartment of Electrical Engineering, National Institute of Technology JamshedpurCollege of Computer and Information Sciences, Prince Sultan UniversityCollege of Computer and Information Sciences, Prince Sultan UniversityCollege of Computer and Information Sciences, Prince Sultan UniversityAbstract Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown dramatically, while road and transportation infrastructure capacities have yet to expand proportionally to handle the additional traffic effectively. Road congestion and traffic-related pollution have increased, which is detrimental to society and public health. This paper proposes a novel reinforcement learning (RL)-based method to reduce traffic congestion. We have developed a sophisticated Deep Q-Network (DQN) and integrated it smoothly into our system. In this study, Our implemented DQL model reduced queue lengths by 49% and increased incentives for each lane by 9%. The results emphasize the effectiveness of our method in setting strong traffic reduction standards. This study shows that RL has excellent potential to improve both transport efficiency and sustainability in metropolitan areas. Moreover, utilizing RL can significantly improve the standards for reducing traffic and easing urban traffic congestion.https://doi.org/10.1038/s41598-024-75638-0Traffic reductionRLSmart cityDQLQueue lengthRewards |
| spellingShingle | S M Masfequier Rahman Swapno SM Nuruzzaman Nobel Preeti Meena V. P. Meena Ahmad Taher Azar Zeeshan Haider Mohamed Tounsi A reinforcement learning approach for reducing traffic congestion using deep Q learning Scientific Reports Traffic reduction RL Smart city DQL Queue length Rewards |
| title | A reinforcement learning approach for reducing traffic congestion using deep Q learning |
| title_full | A reinforcement learning approach for reducing traffic congestion using deep Q learning |
| title_fullStr | A reinforcement learning approach for reducing traffic congestion using deep Q learning |
| title_full_unstemmed | A reinforcement learning approach for reducing traffic congestion using deep Q learning |
| title_short | A reinforcement learning approach for reducing traffic congestion using deep Q learning |
| title_sort | reinforcement learning approach for reducing traffic congestion using deep q learning |
| topic | Traffic reduction RL Smart city DQL Queue length Rewards |
| url | https://doi.org/10.1038/s41598-024-75638-0 |
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