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Traffic prediction is one of the most significant foundations in IntelligentTransportation Systems (ITS). Traditional traffic prediction methods rely onlyon historical traffic data to predict traffic trends and face two mainchallenges. 1) insensitivity to unusual events. 2) poor performance inlong-term prediction. In this work, we explore how generative models combinedwith text describing the traffic system can be applied for traffic generationand name the task Text-to-Traffic Generation (TTG). The key challenge of theTTG task is how to associate text with the spatial structure of the roadnetwork and traffic data for generating traffic situations. To this end, wepropose ChatTraffic, the first diffusion model for text-to-traffic generation.To guarantee the consistency between synthetic and real data, we augment adiffusion model with the Graph Convolutional Network (GCN) to extract spatialcorrelations of traffic data. In addition, we construct a large datasetcontaining text-traffic pairs for the TTG task. We benchmarked our modelqualitatively and quantitatively on the released dataset. The experimentalresults indicate that ChatTraffic can generate realistic traffic situationsfrom the text. Our code and dataset are available athttps://github.com/ChyaZhang/ChatTraffic.

ChatTraffic: Text-to-Traffic Generation via Diffusion Model