Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to redu...

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Bibliographic Details
Published in:Electronics (Basel) 2024-10, Vol.13 (19), p.3875
Main Authors: Agrahari, Anurag, Dhabu, Meera M., Deshpande, Parag S., Tiwari, Ashish, Baig, Mogal Aftab, Sawarkar, Ankush D.
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive systems
Air pollution
Artificial intelligence
Control systems
Deep learning
Dynamic programming
Fuzzy logic
Greenhouse gases
Heuristic methods
Pedestrians
Real time
Road construction
Roads
Roads & highways
Simulation
Traffic accidents & safety
Traffic congestion
Traffic control
Traffic flow
Traffic intersections
Traffic signals
Traffic volume
Transportation systems
Urban areas
Vehicles
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Summary:The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13193875