  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.
