核心概念
Large Language Models (LLMs) enhance traffic signal control by integrating advanced reasoning capabilities, improving efficiency and adaptability in complex urban environments.
摘要
- 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.
統計資料
"Notably, in cases of Sensor Outage (SO), our approach surpasses conventional RL-based systems by reducing the average waiting time by 20.4%."