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A Survey on Traffic Signal Control Methods
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Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now have richer data, more computing power and advanced methods to drive the development of intelligent transportation. With the growing interest in intelligent transportation using machine learning methods like reinforcement learning, this survey covers the widely acknowledged transportation approaches and a comprehensive list of recent literature on reinforcement for traffic signal control. We hope this survey can foster interdisciplinary research on this important topic.
Forward citations
Cited by 5 Pith papers
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Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control
JL-GAT extends grounded action transformation to multi-agent traffic signal control by feeding each agent's grounding models with neighboring state and action information, reducing the sim-to-real gap in simulated rai...
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A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning
A two-level DDPG controller that splits a fixed 60-second traffic signal cycle by direction, then by movement, achieves the lowest average travel time among eight methods in CityFlow simulations.
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SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model
A single diffusion model predicts agent and traffic-light states, including which agents are present, to enable closed-loop trip-level traffic simulation over kilometer-scale maps.
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A Distributionally Robust Multi-agent Reinforcement Learning Framework for Intelligent Intersection Control
An adaptive contextual-bandit worst-case estimator co-trained with MARL traffic controllers cuts worst-case and average queues by large margins on grid and Monaco networks and generalizes zero-shot to unseen demand.
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Green Wave as an Integral Part for the Optimization of Traffic Efficiency and Safety: A Survey
A review of green wave signal control finds that V2X and reinforcement learning are emerging as key enhancements, while scalability and vulnerable road user safety remain open challenges.
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