Traffic signal control method based on large language model, system and computer-readable storage medium

LLMs enhance traffic signal control by integrating them into the framework to address dynamic challenges, improving decision-making and efficiency in complex traffic scenarios.

WO2026040085A1 Publication Date: 2026-02-26THE CHINESE UNIV OF HONG KONG (SHENZHEN)
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Patent Information

Application Number
PCT/CN2024/114239
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Filing Date
2024-08-23
Publication Date
2026-02-26

AI Technical Summary

Technical Problem

Traditional traffic signal control systems, including rule-based and Reinforcement Learning (RL)-based methods, struggle to adapt dynamically to complex and uncommon traffic situations, often leading to inefficiencies and failures in managing unexpected events like emergency vehicle arrivals or sensor outages.

Method used

Integrate Large Language Models (LLMs) into the traffic signal control framework to enhance decision-making by leveraging their extensive knowledge and common-sense reasoning abilities, using a suite of perception and decision tools to analyze traffic state information and determine optimal signal control strategies.

Benefits of technology

Improves road transport efficiency by effectively handling complex and uncommon traffic scenarios, ensuring seamless integration with existing algorithms and maintaining state-of-the-art performance under conventional conditions while adapting to exceptional situations.

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Abstract

The present application provides a traffic signal control method based on Large language model (LLM), system and computer-readable storage medium. The method includes: obtaining traffic state information in a traffic environment; transmitting the traffic state information to the LLM; determining a traffic signal control strategy based on the traffic state information and the LLM; and controlling working states of a plurality of traffic signals based on the traffic signal control strategy. In the present application, the LLM is integrated into the TSC framework to assist in the decision-making process, the extensive knowledge and common-sense reasoning abilities of LLM are leveraged to enhance decision-making in complex and uncommon traffic situations, thereby improving road transport efficiency.
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