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) limited 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来预测交通趋势，面临两个主要挑战：1）对异常事件不敏感；2）在长期预测中性能有限。本文探索如何将生成模型与描述交通系统的文本相结合应用于交通生成，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的空间结构及交通数据关联起来，以生成交通态势。为此，我们提出ChatTraffic——首个用于文本到交通生成的扩散模型。为保证合成数据与真实数据的一致性，我们在扩散模型中融入图卷积网络（GCN）以提取交通数据的空间相关性。此外，我们构建了一个包含文本-交通对的大规模数据集用于TTG任务。我们在公开数据集上对模型进行了定性和定量基准测试。实验结果表明，ChatTraffic能够根据文本生成逼真的交通态势。我们的代码和数据集已开源至https://github.com/ChyaZhang/ChatTraffic。