Optimizing Traffic Light Control using Enhanced DQN: Minimizing Waiting Time for Regular and Emergency Vehicles
An efficient traffic management system is essential to minimize traffic problems and ensure the rapid circulation of emergency vehicles. This research proposes a new single-agent deep reinforcement-learning model using a deep Q-Network (DQN) to optimize traffic lights, aiming to reduce waiting times...
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| Format: | Article |
| Language: | English |
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Sciendo
2025-04-01
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| Series: | Transport and Telecommunication |
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| Online Access: | https://doi.org/10.2478/ttj-2025-0020 |
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| author | Bouzi Wissam Bentaieb Samia Ouamri Abdelaziz |
| author_facet | Bouzi Wissam Bentaieb Samia Ouamri Abdelaziz |
| author_sort | Bouzi Wissam |
| collection | DOAJ |
| description | An efficient traffic management system is essential to minimize traffic problems and ensure the rapid circulation of emergency vehicles. This research proposes a new single-agent deep reinforcement-learning model using a deep Q-Network (DQN) to optimize traffic lights, aiming to reduce waiting times and increase vehicle speed, with particular emphasis on emergency vehicles. Our method incorporates a new state representation, which captures variations in vehicle density and speed that directly influence the reward structure to prioritize both traffic flow and emergency vehicle response times. The decision of the agent is enhanced by a replay memory mechanism, which ensures that experiences are effectively used in learning. The model’s effectiveness was tested in a simulated environment using SUMO, showing significant improvements in traffic management compared to traditional methods. Experimental results show that our system significantly reduces average waiting times and improves emergency vehicle prioritization. |
| format | Article |
| id | doaj-art-71a78501b7cf4d1980ee6aed65a2fcfe |
| institution | OA Journals |
| issn | 1407-6179 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Transport and Telecommunication |
| spelling | doaj-art-71a78501b7cf4d1980ee6aed65a2fcfe2025-08-20T02:38:38ZengSciendoTransport and Telecommunication1407-61792025-04-0126326627510.2478/ttj-2025-0020Optimizing Traffic Light Control using Enhanced DQN: Minimizing Waiting Time for Regular and Emergency VehiclesBouzi Wissam0Bentaieb Samia1Ouamri Abdelaziz2Laboratory Signals and Images, Faculty of Electrical Engineering, Department of Electronics, University of Sciences and Technology of Oran Mohamed Boudiaf USTO-MB Oran, Algeria, B.P. 1505, El Mnaouar – Bir el Djir2Faculty of Science and Technology, Department of Electronics and TelecommunicationsUniversity of Ain Temouchent Behadj Bouchaib Ain Temouchent, Algeria, Road of Sidi Bel Abes N101, 46000Laboratory Signals and Images, Faculty of Electrical Engineering, Department of Electronics, University of Sciences and Technology of Oran Mohamed Boudiaf USTO-MB Oran, Algeria, B.P. 1505, El Mnaouar – Bir el DjirAn efficient traffic management system is essential to minimize traffic problems and ensure the rapid circulation of emergency vehicles. This research proposes a new single-agent deep reinforcement-learning model using a deep Q-Network (DQN) to optimize traffic lights, aiming to reduce waiting times and increase vehicle speed, with particular emphasis on emergency vehicles. Our method incorporates a new state representation, which captures variations in vehicle density and speed that directly influence the reward structure to prioritize both traffic flow and emergency vehicle response times. The decision of the agent is enhanced by a replay memory mechanism, which ensures that experiences are effectively used in learning. The model’s effectiveness was tested in a simulated environment using SUMO, showing significant improvements in traffic management compared to traditional methods. Experimental results show that our system significantly reduces average waiting times and improves emergency vehicle prioritization.https://doi.org/10.2478/ttj-2025-0020intelligent traffic light controlemergency vehiclewaiting timespeeddqn |
| spellingShingle | Bouzi Wissam Bentaieb Samia Ouamri Abdelaziz Optimizing Traffic Light Control using Enhanced DQN: Minimizing Waiting Time for Regular and Emergency Vehicles Transport and Telecommunication intelligent traffic light control emergency vehicle waiting time speed dqn |
| title | Optimizing Traffic Light Control using Enhanced DQN: Minimizing Waiting Time for Regular and Emergency Vehicles |
| title_full | Optimizing Traffic Light Control using Enhanced DQN: Minimizing Waiting Time for Regular and Emergency Vehicles |
| title_fullStr | Optimizing Traffic Light Control using Enhanced DQN: Minimizing Waiting Time for Regular and Emergency Vehicles |
| title_full_unstemmed | Optimizing Traffic Light Control using Enhanced DQN: Minimizing Waiting Time for Regular and Emergency Vehicles |
| title_short | Optimizing Traffic Light Control using Enhanced DQN: Minimizing Waiting Time for Regular and Emergency Vehicles |
| title_sort | optimizing traffic light control using enhanced dqn minimizing waiting time for regular and emergency vehicles |
| topic | intelligent traffic light control emergency vehicle waiting time speed dqn |
| url | https://doi.org/10.2478/ttj-2025-0020 |
| work_keys_str_mv | AT bouziwissam optimizingtrafficlightcontrolusingenhanceddqnminimizingwaitingtimeforregularandemergencyvehicles AT bentaiebsamia optimizingtrafficlightcontrolusingenhanceddqnminimizingwaitingtimeforregularandemergencyvehicles AT ouamriabdelaziz optimizingtrafficlightcontrolusingenhanceddqnminimizingwaitingtimeforregularandemergencyvehicles |