Traffic prediction is one of the most significant foundations in Intelligent Transportation Systems (ITS). Traditional traffic prediction methods rely only on historical traffic data to predict traffic trends and face two main challenges. 1) insensitivity to unusual events. 2) poor performance in long-term prediction. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation and name the task Text-to-Traffic Generation (TTG). The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations. To this end, we propose ChatTraffic, the first diffusion model for text-to-traffic generation. To guarantee the consistency between synthetic and real data, we augment a diffusion model with the Graph Convolutional Network (GCN) to extract spatial correlations of traffic data. In addition, we construct a large dataset containing text-traffic pairs for the TTG task. We benchmarked our model qualitatively and quantitatively on the released dataset. The experimental results indicate that ChatTraffic can generate realistic traffic situations from the text. Our code and dataset are available at https://github.com/ChyaZhang/ChatTraffic.

翻译：交通预测是智能交通系统（ITS）中最关键的基石之一。传统交通预测方法仅依赖历史交通数据预测趋势，面临两大挑战：1）对异常事件不敏感；2）长期预测性能较差。本研究探索生成模型结合描述交通系统的文本应用于交通生成，并将该任务命名为文本到交通生成（TTG）。TTG任务的核心挑战在于如何将文本与道路网络空间结构及交通数据相关联以生成交通场景。为此，我们提出ChatTraffic——首个用于文本到交通生成的扩散模型。为保障合成数据与真实数据的一致性，我们在扩散模型中增广图卷积网络（GCN）以提取交通数据的空间相关性。此外，我们构建了包含文本-交通对的大规模数据集以支持TTG任务。我们在公开数据集上对模型进行了定性与定量基准测试，实验结果表明ChatTraffic能够根据文本生成逼真的交通场景。相关代码与数据集已发布在https://github.com/ChyaZhang/ChatTraffic。