mlsanityData to 14 Oct 2026

ChatTraffic: Text-to-Traffic Generation via Diffusion Model

arXiv 2311.16203 ↗First posted 27 Nov 2023cs.LG cs.AI cs.CLcited by 7

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 48 of its 82 references that are in the map. First posted on arXiv 27 Nov 2023.

Builds on

48 of 82 references.

References · 48

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36 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2024A Survey of Generative Techniques for Spatial-Temporal Data Miningcited by 14
  2. 2025Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Reviewcited by 8
  3. 2025UrbanMind: Urban Dynamics Prediction with Multifaceted Spatial-Temporal Large Language Modelscited by 2
  4. 2026Net-Ev2: A Generative Simulator for Network Event Evolutioncited by 1
  5. 2025CrossTrafficLLM: A Human-Centric Framework for Interpretable Traffic Intelligence via Large Language Modelcited by 0
  6. 2026RiskTraf: Risk-Extrapolated Residual Learning for Multi-Variate Traffic Flow Predictioncited by 0
  7. 2026DSWM: Decomposed Spatio-Temporal World Model for Demand-Driven UAV Base Station Repositioningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024BjTT: A Large-scale Multimodal Dataset for Traffic Prediction
  2. 2024STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting
  3. 2023Language-Guided Traffic Simulation via Scene-Level Diffusion
  4. 2025FlowDistill: Scalable Traffic Flow Prediction via Distillation from LLMs
  5. 2026A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting
  6. 2026TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation
  7. 2026Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories
  8. 2025EP-Diffuser: An Efficient Diffusion Model for Traffic Scene Generation and Prediction via Polynomial Representations