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arXiv Machine Learning By Xue Qiu, Jianli Xiao

A Dynamic Fusion Large Language Model for Traffic Flow Prediction

Read the original on arXiv Machine Learning →

The paper introduces DF-LLM, a Dynamic Fusion Large Language Model designed for traffic flow prediction. It combines a spatiotemporal embedding module, a fusion module that uses graph convolution to capture spatial topology and dynamic dependencies, and an LLM backbone with differentiated parameter adaptation and context aggregation attention. Experiments on four datasets demonstrate that DF-LLM outperforms existing methods in predictive accuracy.

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