CrossTrafficLLM: A Human-Centric Framework for Interpretable Traffic Intelligence via Large Language Model

TL;DR

CrossTrafficLLM introduces a unified framework that predicts traffic states and generates natural language summaries, enhancing interpretability and decision support in intelligent transportation systems.

Contribution

It is the first to integrate traffic forecasting with natural language report generation using large language models in a unified architecture.

Findings

Outperforms state-of-the-art in traffic prediction accuracy.

Generates high-quality, human-readable traffic reports.

Improves interpretability and usability of traffic data.

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.