Fuzzy logic and deep Q learning based control for traffic lights
Traffic congestion is a major concern for many metropolises. Although it is difficult to regulate traffic flow because of numerous complexities and uncertainties, the traffic congestion problem must be mitigated in order to reduce the environmental problems related to traffic and the time lost on th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-03-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016822008122 |
| _version_ | 1797884511478874112 |
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| author | Ilhan Tunc Mehmet Turan Soylemez |
| author_facet | Ilhan Tunc Mehmet Turan Soylemez |
| author_sort | Ilhan Tunc |
| collection | DOAJ |
| description | Traffic congestion is a major concern for many metropolises. Although it is difficult to regulate traffic flow because of numerous complexities and uncertainties, the traffic congestion problem must be mitigated in order to reduce the environmental problems related to traffic and the time lost on the roads in big cities. Intelligent traffic control methods, the use of which is increasing with the development of new methods, as opposed to conventional methods, and provide more efficient solutions, especially in traffic intersections with high traffic density. In this paper, we propose a new agent-based Fuzzy Logic assisted traffic light signal timing for traffic intersections. Deep Q-Learning algorithms and Fuzzy Logic Control (FLC) are used together in the proposed method. In this study, the proposed method and many traffic light control methods in the literature were simulated. In order to demonstrate the effectiveness of the proposed method, some of the important metrics of evaluation such as traffic congestion, air pollution, and waiting time were used in the assessment of the simulation results. In addition, with the proposed method, it has been shown that the stability and robustness of the system are increased. |
| first_indexed | 2024-04-10T04:08:48Z |
| format | Article |
| id | doaj.art-db025f50600644da952b00325a8d5fee |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-10T04:08:48Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-db025f50600644da952b00325a8d5fee2023-03-13T04:15:14ZengElsevierAlexandria Engineering Journal1110-01682023-03-0167343359Fuzzy logic and deep Q learning based control for traffic lightsIlhan Tunc0Mehmet Turan Soylemez1Department of Mechatronics Engineering, Bursa Technical University, Bursa, Turkey; Department of Control and Automation Engineering, Istanbul Technical University, Istanbul, Turkey; Corresponding author at: Department of Mechatronics Engineering, Bursa Technical University, Bursa, Turkey.Department of Control and Automation Engineering, Istanbul Technical University, Istanbul, TurkeyTraffic congestion is a major concern for many metropolises. Although it is difficult to regulate traffic flow because of numerous complexities and uncertainties, the traffic congestion problem must be mitigated in order to reduce the environmental problems related to traffic and the time lost on the roads in big cities. Intelligent traffic control methods, the use of which is increasing with the development of new methods, as opposed to conventional methods, and provide more efficient solutions, especially in traffic intersections with high traffic density. In this paper, we propose a new agent-based Fuzzy Logic assisted traffic light signal timing for traffic intersections. Deep Q-Learning algorithms and Fuzzy Logic Control (FLC) are used together in the proposed method. In this study, the proposed method and many traffic light control methods in the literature were simulated. In order to demonstrate the effectiveness of the proposed method, some of the important metrics of evaluation such as traffic congestion, air pollution, and waiting time were used in the assessment of the simulation results. In addition, with the proposed method, it has been shown that the stability and robustness of the system are increased.http://www.sciencedirect.com/science/article/pii/S1110016822008122Deep Q learningFuzzy logic controlTraffic light control |
| spellingShingle | Ilhan Tunc Mehmet Turan Soylemez Fuzzy logic and deep Q learning based control for traffic lights Alexandria Engineering Journal Deep Q learning Fuzzy logic control Traffic light control |
| title | Fuzzy logic and deep Q learning based control for traffic lights |
| title_full | Fuzzy logic and deep Q learning based control for traffic lights |
| title_fullStr | Fuzzy logic and deep Q learning based control for traffic lights |
| title_full_unstemmed | Fuzzy logic and deep Q learning based control for traffic lights |
| title_short | Fuzzy logic and deep Q learning based control for traffic lights |
| title_sort | fuzzy logic and deep q learning based control for traffic lights |
| topic | Deep Q learning Fuzzy logic control Traffic light control |
| url | http://www.sciencedirect.com/science/article/pii/S1110016822008122 |
| work_keys_str_mv | AT ilhantunc fuzzylogicanddeepqlearningbasedcontrolfortrafficlights AT mehmetturansoylemez fuzzylogicanddeepqlearningbasedcontrolfortrafficlights |