用大模型提升交通流量预测适应力，动态选最优结果。
Embracing Large Language Models in Traffic Flow Forecasting
- 双分支图与超图结构捕捉时空关系，分别预训练
- 测试时由大模型从两分支结果中选出最可能的预测
- 适合需应对突发交通变化的智能交通系统
交通流量预测旨在基于历史交通状态和道路网络预测未来流量，是智能交通系统中的重要问题。现有方法主要关注捕捉时空依赖关系，但在测试时面对环境变化的适应性不足。为此，我们提出引入大语言模型（LLM）增强交通流量预测，设计新方法LEAF。LEAF采用双分支结构，分别利用图与超图结构捕捉不同类型的时空关系，两分支先独立预训练；测试时生成不同预测结果，由大语言模型选择最可能的输出。同时使用排名损失作为学习目标，提升两分支的预测能力。在多个数据集上的大量实验验证了LEAF的有效性。
原文摘要 · Abstract (English)
Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network. It is an important problem in intelligent transportation systems, with a plethora of methods been proposed. Existing efforts mainly focus on capturing and utilizing spatio-temporal dependencies to predict future traffic flows. Though promising, they fall short in adapting to test-time environmental changes of traffic conditions. To tackle this challenge, we propose to introduce large language models (LLMs) to help traffic flow forecasting and design a novel method named Large Language Model Enhanced Traffic Flow Predictor (LEAF). LEAF adopts two branches, capturing different spatio-temporal relations using graph and hypergraph structures respectively. The two branches are first pre-trained individually, and during test-time, they yield different predictions. Based on these predictions, a large language model is used to select the most likely result. Then, a ranking loss is applied as the learning objective to enhance the prediction ability of the two branches. Extensive experiments on several datasets demonstrate the effectiveness of the proposed LEAF.
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