Learning Traffic Signal Control via Genetic Programming: A New Approach for Efficient Traffic Signal Control
Proposing a new approach using genetic programming for efficient and explainable traffic signal control.
LLM-Assisted Light: Integrating Large Language Models for Traffic Signal Control
Large Language Models (LLMs) enhance traffic signal control by integrating advanced reasoning capabilities, improving efficiency and adaptability in complex urban environments.
Leveraging Large Language Models for Traffic Signal Control
The author introduces an innovative approach integrating Large Language Models (LLMs) into Traffic Signal Control (TSC) systems to enhance decision-making in complex traffic scenarios, highlighting the potential of LLMs to revolutionize traffic management.
Vision-Based Traffic Signal Control Framework with Microscopic Simulation
The author presents a holistic framework for vision-based traffic signal control using microscopic simulation, emphasizing the potential of end-to-end learning and optimization of traffic signals.
LLMLight: Large Language Models as Traffic Signal Control Agents
The authors introduce LLMLight, a framework utilizing Large Language Models (LLMs) for traffic signal control, emphasizing effective decision-making and generalization capabilities. They propose LightGPT, a specialized LLM tailored for TSC tasks, showcasing superior effectiveness and interpretability.
