English(EN) Benchmarking Retrieval-Augmented Generation Strategies for Large Language Model-Based Travel Mode Choice Prediction

结合RAG的大型语言模型提升出行模式预测准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员开发了一个新的框架，利用检索增强生成（RAG）增强的大型语言模型（LLMs）来预测出行模式选择。该研究评估了四种RAG策略和三种LLM架构，包括OpenAI的GPT-4o、o4-mini和o3。结果表明，RAG显著提高了预测准确性，其中GPT-4o模型结合平衡检索和交叉编码器重排序实现了80.8%的最高准确率。与传统方法相比，该方法还表现出优越的零样本迁移能力。 AI

影响证明了大型语言模型可以显著提高交通规划等专业领域的预测准确性。

排序理由详细介绍新方法和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Yiming Xu, Junfeng Jiao · 2026-08-25 04:00

大型语言模型旅行模式选择预测的检索增强生成策略基准测试

arXiv:2508.17527v2 Announce Type: replace Abstract: Accurately predicting travel mode choice is essential for effective transportation planning, yet traditional statistical and machine learning models are constrained by rigid assumptions, limited contextual reasoning, and reduced…

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大型语言模型旅行模式选择预测的检索增强生成策略基准测试

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