ChatTraffic: Text-to-Traffic Generation via Diffusion Model

The analysis of traffic situations under abnormal conditions is one of the bottleneck issues in Intelligent Transportation Systems (ITS). Influenced by the suddenness, randomness, and uncertainty, this issue is challenging to achieve through existing deep learning methods. It needs to be assisted by traffic simulation models for analysis. However, simulation models always require extensive scene modeling and calibration, making it difficult to meet the demands of natural human-machine interaction in the AIGC (Artificial Intelligence Generated Content) era, as well as the need for rapid and flexible implementation of situation analysis. With the accumulation of traffic data, the emergence of diffusion models offers a new entry point for the core method of data-driven analysis, namely Text-to-Traffic Generation (TTG). In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic situation generation, and propose ChatTraffic, the first diffusion model for TTG. 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-scale dataset containing text-traffic pairs for TTG. We benchmarked ChatTraffic qualitatively and quantitatively on the released dataset. The experimental results indicate that ChatTraffic can rapidly and flexibly generate realistic traffic situations from text, which have practical significance in addressing bottlenecks in ITS. Our code and dataset are available at https://github.com/ChyaZhang/ChatTraffic.