Edge Computing Enabled Large-Scale Traffic Flow Prediction With GPT in Intelligent Autonomous Transport System for 6G Network

Abstract

The Intelligent Autonomous Transport System in 6G (6G-IATS) refers to the coordination of 6G, Artificial Intelligence (AI), and intelligent transportation systems, which is expected to revolutionize future intelligent transportation systems. In 6G-IATS, large-scale traffic flow prediction, affiliated with time series prediction, holds significant value for transportation planning and urban management. As an emerging AI method, Large Language Models (LLMs) have emerged prominently in time series forecasting. Unfortunately, it is challenging to achieve accurate and efficient large-scale traffic flow prediction by LLMs in 6G-IATS, due to the two issues: a) these LLMs fail to capture the spatio-temporal correlations in a large-scale road network, leading to limited prediction accuracy, and b) they process a substantial amount of training data on the central server, which imposes low training efficiency. Jointly considering the two concerns, this paper proposes a novel LLM and edge computing-based architecture for large-scale traffic flow prediction in 6G-IATS, called Spatio-Temporal Generative Large Language Model on Edge (STGLLM-E). In this architecture, we first decompose the entire large-scale road network into several subgraphs. To capture the spatio-temporal correlations, an LLM-based method named Spatio-Temporal Generative Large Language Model (STGLLM) including Spatio-Temporal Module (STM) and Generative Large Language Model (GLLM) is proposed. Secondly, to improve the training efficiency of the STGLLM-E, an edge training strategy based on edge servers is devised. Experiments are conducted on two real-world traffic flow datasets. The experimental results illustrate that the STGLLM-E is superior to the baselines in the prediction accuracy and the efficiency of training.