Abstract
While accurate traffic forecasting is vital for Intelligent Transportation Systems (ITS), effectively communicating predicted conditions via natural language for human-centric decision support remains a challenge and is often handled separately. To address this, we propose CrossTrafficLLM, a novel GenAI-driven framework that simultaneously predicts future spatiotemporal traffic states and generates corresponding natural language descriptions, specifically targeting conditional abnormal event summaries. We tackle the core challenge of aligning quantitative traffic data with qualitative textual semantics by leveraging Large Language Models (LLMs) within a unified architecture. This design allows generative textual context to improve prediction accuracy while ensuring generated reports are directly informed by the forecast. Technically, a text-guided adaptive graph convolutional network is employed to effectively merge high-level semantic information with the traffic network structure. Evaluated on the BJTT dataset, CrossTrafficLLM demonstrably surpasses state-of-the-art methods in both traffic forecasting performance and text generation quality. By unifying prediction and description generation, CrossTrafficLLM delivers a more interpretable, and actionable approach to generative traffic intelligence, offering significant advantages for modern ITS applications.
Abstract (translated)
虽然准确的交通预测对于智能交通系统（ITS）至关重要，但通过自然语言有效传达预测情况以支持以人为中心的决策仍然是一项挑战，并且通常被单独处理。为了解决这个问题，我们提出了CrossTrafficLLM，这是一种新颖的基于生成AI（GenAI）的框架，该框架能够同时预测未来的时空交通状态并生成相应的自然语言描述，特别针对条件异常事件总结。我们通过利用大型语言模型（LLMs）在统一架构中解决将定量交通数据与定性文本语义对齐的核心挑战。这种设计允许生成性的文本上下文提高预测准确性，并确保生成的报告直接基于预测结果进行信息补充。 从技术角度来看，CrossTrafficLLM采用了由文本引导的自适应图卷积网络来有效地融合高级语义信息和交通网络结构。在BJTT数据集上的评估表明，CrossTrafficLLM在交通预测性能和文本生成质量方面均超越了现有的最先进的方法。通过统一预测和描述生成过程，CrossTrafficLLM提供了一种更具解释性和可操作性的生成性交通智能方法，并为现代ITS应用带来了显著的优势。
URL
https://arxiv.org/abs/2601.06042
