用大模型预测交通事故影响，无需大量训练数据。
Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents
- 结合交通特征与大模型提取的事件特征进行预测
- 最佳大模型性能媲美最准确的机器学习模型
- 适合缺乏标注数据的交通管理场景
本研究探讨了将大语言模型（LLMs）应用于预测交通事故对交通流影响的可行性。相比现有基于机器学习的方案，该方法无需大规模训练数据，且可利用自由文本事故日志。我们提出一种全基于大模型的解决方案，通过融合交通特征与大模型提取的事故特征进行预测，并设计了一种有效的上下文学习示例选择方法。在真实交通事件数据集上，评估了三种先进大模型与两种先进机器学习模型的性能。结果表明，表现最佳的大模型在未针对该任务训练的情况下，其预测精度与最准确的机器学习模型相当。研究结果表明，大语言模型是交通事件影响预测的一种切实可行的选择。
原文摘要 · Abstract (English)
This study examines the feasibility of applying large language models (LLMs) for forecasting the impact of traffic incidents on the traffic flow. The use of LLMs for this task has several advantages over existing machine learning-based solutions such as not requiring a large training dataset and the ability to utilize free-text incident logs. We propose a fully LLM-based solution that predicts the incident impact using a combination of traffic features and LLM-extracted incident features. A key ingredient of this solution is an effective method of selecting examples for the LLM's in-context learning. We evaluate the performance of three advanced LLMs and two state-of-the-art machine learning models on a real traffic incident dataset. The results show that the best-performing LLM matches the accuracy of the most accurate machine learning model, despite the former not having been trained on this prediction task. The findings indicate that LLMs are a practically viable option for traffic incident impact prediction.
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