Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning
In recent years, rapid urbanization has led to increased traffic congestion, rendering traditional traffic light control methods ineffective. Deep Reinforcement Learning (DRL) has emerged as a promising approach to sequential decision-making, offering adaptive and efficient solutions for traffic man...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11029249/ |
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| author | Zahra Fereidooni Luciano Alessandro Ipsaro Palesi Paolo Nesi |
| author_facet | Zahra Fereidooni Luciano Alessandro Ipsaro Palesi Paolo Nesi |
| author_sort | Zahra Fereidooni |
| collection | DOAJ |
| description | In recent years, rapid urbanization has led to increased traffic congestion, rendering traditional traffic light control methods ineffective. Deep Reinforcement Learning (DRL) has emerged as a promising approach to sequential decision-making, offering adaptive and efficient solutions for traffic management. This paper aims to develop an optimal traffic light planning strategy that integrates seamlessly with urban transportation systems, including trams and Bus Rapid Transit Systems (BRTS). The study explores three DRL-based approaches: Single-Agent Deep Reinforcement Learning (SADRL), Multi-Agent Deep Reinforcement Learning (MADRL) with fixed traffic lights, and an actuated control approach. System for Managing Actuated and Real-Time Traffic, referred to as SMART, dynamically adjusts traffic signals based on real-time conditions to enhance traffic flow efficiency. The proposed methods are evaluated and compared against the Webster method, Simulation of Urban Mobility (SUMO)-based control, and a genetic algorithm-based multi-objective traffic light optimization method (MamoTLO). The results demonstrate that DRL-based solutions improve traffic flow and reduce congestion. |
| format | Article |
| id | doaj-art-2d0a13d45bf4485d86810beb396ffc6a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2d0a13d45bf4485d86810beb396ffc6a2025-08-20T03:24:06ZengIEEEIEEE Access2169-35362025-01-011310697410698810.1109/ACCESS.2025.357851811029249Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement LearningZahra Fereidooni0Luciano Alessandro Ipsaro Palesi1https://orcid.org/0000-0001-8992-2084Paolo Nesi2https://orcid.org/0000-0003-1044-3107Department of Information Engineering, DISIT Laboratory, University of Florence, Florence, ItalyDepartment of Information Engineering, DISIT Laboratory, University of Florence, Florence, ItalyDepartment of Information Engineering, DISIT Laboratory, University of Florence, Florence, ItalyIn recent years, rapid urbanization has led to increased traffic congestion, rendering traditional traffic light control methods ineffective. Deep Reinforcement Learning (DRL) has emerged as a promising approach to sequential decision-making, offering adaptive and efficient solutions for traffic management. This paper aims to develop an optimal traffic light planning strategy that integrates seamlessly with urban transportation systems, including trams and Bus Rapid Transit Systems (BRTS). The study explores three DRL-based approaches: Single-Agent Deep Reinforcement Learning (SADRL), Multi-Agent Deep Reinforcement Learning (MADRL) with fixed traffic lights, and an actuated control approach. System for Managing Actuated and Real-Time Traffic, referred to as SMART, dynamically adjusts traffic signals based on real-time conditions to enhance traffic flow efficiency. The proposed methods are evaluated and compared against the Webster method, Simulation of Urban Mobility (SUMO)-based control, and a genetic algorithm-based multi-objective traffic light optimization method (MamoTLO). The results demonstrate that DRL-based solutions improve traffic flow and reduce congestion.https://ieeexplore.ieee.org/document/11029249/Deep reinforcement learningsmart citiestraffic controltraffic lightmulti-agentsingle-agent |
| spellingShingle | Zahra Fereidooni Luciano Alessandro Ipsaro Palesi Paolo Nesi Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning IEEE Access Deep reinforcement learning smart cities traffic control traffic light multi-agent single-agent |
| title | Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning |
| title_full | Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning |
| title_fullStr | Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning |
| title_full_unstemmed | Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning |
| title_short | Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning |
| title_sort | multi agent optimizing traffic light signals using deep reinforcement learning |
| topic | Deep reinforcement learning smart cities traffic control traffic light multi-agent single-agent |
| url | https://ieeexplore.ieee.org/document/11029249/ |
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