Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination
This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to optimize traffic signal control, reduce congestion, and enhance safety. Utilizing existing road infrastructure, VLC technology transmits real-time data on vehicle and pedestrian positions, speeds, and queues. A...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/116 |
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| author | Manuela Vieira Gonçalo Galvão Manuel A. Vieira Mário Vestias Paula Louro Pedro Vieira |
| author_facet | Manuela Vieira Gonçalo Galvão Manuel A. Vieira Mário Vestias Paula Louro Pedro Vieira |
| author_sort | Manuela Vieira |
| collection | DOAJ |
| description | This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to optimize traffic signal control, reduce congestion, and enhance safety. Utilizing existing road infrastructure, VLC technology transmits real-time data on vehicle and pedestrian positions, speeds, and queues. AI agents, powered by Deep Reinforcement Learning (DRL), process these data to manage traffic flows dynamically, applying anti-bottlenecking and rerouting techniques. A global agent coordinates local agents, enabling indirect communication and a unified DRL model that adjusts traffic light phases in real time using a queue/request/response system. A key focus of this work is the design of reward functions for standard and rerouting scenarios. In standard scenarios, the reward function prioritizes wide green bands for vehicles while penalizing pedestrian rule violations, balancing efficiency and safety. In rerouting scenarios, it dynamically prevents queuing spillovers at neighboring intersections, mitigating cascading congestion and ensuring safe, timely pedestrian crossings. Simulation experiments in the SUMO urban mobility simulator and real-world trials validate the system across diverse intersection types, including four-way crossings, T-intersections, and roundabouts. Results show significant reductions in vehicle and pedestrian waiting times, particularly in rerouting scenarios, demonstrating the system’s scalability and adaptability. By integrating VLC technology and AI-driven adaptive control, this approach achieves efficient, safe, and flexible traffic management. The proposed system addresses urban mobility challenges effectively, offering a robust solution to modern traffic demands while improving the travel experience for all road users. |
| format | Article |
| id | doaj-art-228799ad140f45f18c1f886d1c19cebb |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-228799ad140f45f18c1f886d1c19cebb2025-01-10T13:14:30ZengMDPI AGApplied Sciences2076-34172024-12-0115111610.3390/app15010116Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting CoordinationManuela Vieira0Gonçalo Galvão1Manuel A. Vieira2Mário Vestias3Paula Louro4Pedro Vieira5DEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, PortugalDEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, PortugalDEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, PortugalDEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, PortugalDEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, PortugalDEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, PortugalThis study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to optimize traffic signal control, reduce congestion, and enhance safety. Utilizing existing road infrastructure, VLC technology transmits real-time data on vehicle and pedestrian positions, speeds, and queues. AI agents, powered by Deep Reinforcement Learning (DRL), process these data to manage traffic flows dynamically, applying anti-bottlenecking and rerouting techniques. A global agent coordinates local agents, enabling indirect communication and a unified DRL model that adjusts traffic light phases in real time using a queue/request/response system. A key focus of this work is the design of reward functions for standard and rerouting scenarios. In standard scenarios, the reward function prioritizes wide green bands for vehicles while penalizing pedestrian rule violations, balancing efficiency and safety. In rerouting scenarios, it dynamically prevents queuing spillovers at neighboring intersections, mitigating cascading congestion and ensuring safe, timely pedestrian crossings. Simulation experiments in the SUMO urban mobility simulator and real-world trials validate the system across diverse intersection types, including four-way crossings, T-intersections, and roundabouts. Results show significant reductions in vehicle and pedestrian waiting times, particularly in rerouting scenarios, demonstrating the system’s scalability and adaptability. By integrating VLC technology and AI-driven adaptive control, this approach achieves efficient, safe, and flexible traffic management. The proposed system addresses urban mobility challenges effectively, offering a robust solution to modern traffic demands while improving the travel experience for all road users.https://www.mdpi.com/2076-3417/15/1/116Visible Light Communication (VLC)Artificial Intelligence (AI)Deep Reinforcement Learning (DRL)traffic signal controltraffic managementreward functions |
| spellingShingle | Manuela Vieira Gonçalo Galvão Manuel A. Vieira Mário Vestias Paula Louro Pedro Vieira Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination Applied Sciences Visible Light Communication (VLC) Artificial Intelligence (AI) Deep Reinforcement Learning (DRL) traffic signal control traffic management reward functions |
| title | Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination |
| title_full | Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination |
| title_fullStr | Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination |
| title_full_unstemmed | Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination |
| title_short | Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination |
| title_sort | integrating visible light communication and ai for adaptive traffic management a focus on reward functions and rerouting coordination |
| topic | Visible Light Communication (VLC) Artificial Intelligence (AI) Deep Reinforcement Learning (DRL) traffic signal control traffic management reward functions |
| url | https://www.mdpi.com/2076-3417/15/1/116 |
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