Core Concepts
Large Language Models (LLMs) enhance traffic signal control by integrating advanced reasoning capabilities, improving efficiency and adaptability in complex urban environments.
Abstract
Traffic congestion in metropolitan areas is a significant challenge with economic, environmental, and social impacts.
Conventional traffic signal control systems have limitations in adapting to dynamic traffic scenarios.
The integration of Large Language Models (LLMs) into traffic signal control improves decision-making and system transparency.
LA-Light framework combines LLMs with perception and decision-making tools for efficient traffic management.
Simulation results show LA-Light's effectiveness in adjusting to various traffic scenarios without additional training.
LA-Light outperforms conventional RL-based systems, reducing waiting time by 20.4% during Sensor Outage scenarios.
Introduction:
Traffic congestion poses challenges globally, emphasizing the need for effective traffic signal control systems.
Conventional TSC Systems:
Traditional rule-based methods like Webster and SOTL have limitations in adapting to changing traffic patterns.
Evolution to RL-Based Systems:
Reinforcement Learning (RL) systems offer flexibility but may struggle with infrequent critical events.
Integration of LLMs:
The LA-Light framework integrates Large Language Models (LLMs) into TSC for improved decision-making.
Framework Design:
LA-Light utilizes perception and decision-making tools to enhance the TSC process with LLM reasoning capabilities.
Simulation Results:
Experiments demonstrate LA-Light's adeptness in adjusting to various traffic scenarios without additional training.
Stats
"Notably, in cases of Sensor Outage (SO), our approach surpasses conventional RL-based systems by reducing the average waiting time by 20.4%."