mlsanityData to 7 Oct 2026

BjTT: A Large-scale Multimodal Dataset for Traffic Prediction

arXiv 2403.05029 ↗First posted 8 Mar 2024cs.AIcited by 6

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 6 papers in the map (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 13 of its 39 references that are in the map. First posted on arXiv 8 Mar 2024.

Builds on

13 of 39 references.

References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  7. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
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  9. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  10. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  11. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  12. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
1 more
  1. 2020From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Datacited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learningcited by 16
  2. 2025Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approachcited by 4
  3. 2025Event-CausNet: Unlocking Causal Knowledge from Text with Large Language Models for Reliable Spatio-Temporal Forecastingcited by 1
  4. 2026SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoningcited by 1
  5. 2025Metropolis-Scale Road Network Datasets for Fine-Grained Urban Traffic Modelingcited by 0
  6. 2025CrossTrafficLLM: A Human-Centric Framework for Interpretable Traffic Intelligence via Large Language Modelcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023ChatTraffic: Text-to-Traffic Generation via Diffusion Model
  2. 2024STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting
  3. 2026A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting
  4. 2024TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts
  5. 2026FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction
  6. 2026GAMMA-Net: Adaptive Long-Horizon Traffic Spatio-Temporal Forecasting Model based on Interleaved Graph Attention and Multi-Axis Mamba
  7. 2024Robust Traffic Forecasting against Spatial Shift over Years
  8. 2025RAST: A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction