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A Survey on Traffic Signal Control Methods
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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 11 Pith papers
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OSM+ is a new open billion-vertex worldwide road network graph dataset derived from OpenStreetMap, accompanied by 31-city traffic prediction and six-city policy control benchmarks.
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EvolveSignal: A Large Language Model Powered Coding Agent for Discovering Traffic Signal Control Strategies
EvolveSignal applies LLM-driven evolutionary program synthesis to discover heuristic variations of traffic signal control logic that reduce delay and stops compared to Webster's method in simulation.
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Momentum Based Reward Design for Low Emission Traffic Signal Control
A progressive multi-turn text-to-vis agent with rule-guided ReAct validation beats one-shot baselines by large execution-accuracy margins on a new reverse-constructed benchmark.
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ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control
ReasonLight uses multimodal foundation models to refine RL-proposed traffic signal phases based on camera images and sensor data, enabling zero-shot adaptation to unseen events such as emergency vehicle priority.
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TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control
TrafficClaw creates a single runtime environment for heterogeneous urban traffic subsystems and deploys an LLM agent with spatiotemporal reasoning to deliver robust control that generalizes across unseen scenarios.
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TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control
An 8B LLM agent with code-based spatiotemporal analytics, procedural memory, and multi-stage agentic RL coordinates six coupled traffic-control tasks across three NYC boroughs in SUMO.
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Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models
AMM separates domain-specific observation adapters from a meta-learned shared dynamics model to enable transferable planning under observation mismatch in traffic signal control.
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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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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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Momentum Based Reward Design for Low Emission Traffic Signal Control
A momentum-based reward for DRL traffic signal control yields better throughput-emission trade-offs and more stable learning than delay or queue rewards in SUMO simulations.
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