BjTT: A Large-scale Multimodal Dataset for Traffic Prediction

Chengyang Zhang1  Yong Zhang1  Qitan Shao1  Jiangtao Feng1  Bo Li1  Yisheng Lv2
  Xinglin Piao1  Baocai Yin1     
1. Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, China
2. Institute of Automation, Chinese Academy of Sciences, China
zhangyong2010@bjut.edu.cn

Up: The traffic event textual description, including time, data type, and location. Down: The visualization of velocity and congestion level.

Abstract


Traffic prediction plays a significant role in Intelligent Transportation Systems (ITS). Although many datasets have been introduced to support the study of traffic prediction, most of them only provide time-series traffic data. However, urban transportation systems are always susceptible to various factors, including unusual weather and traffic accidents. Therefore, relying solely on historical data for traffic prediction greatly limits the accuracy of the prediction. In this paper, we introduce Beijing Text-Traffic (BjTT), a large-scale multimodal dataset for traffic prediction. BjTT comprises over 32,000 time-series traffic records, capturing velocity and congestion levels on more than 1,200 roads within the 5th ring area of Beijing. Meanwhile, each piece of traffic data is coupled with a text describing the traffic system (including time, location, and events). We detail the data collection and processing procedures and present a statistical analysis of the BjTT dataset. Furthermore, we conduct comprehensive experiments on the dataset with state-of-the-art traffic prediction methods and text-guided generative models, which reveal the unique characteristics of the BjTT.

BjTT Overview



Statistics of BjTT dataset.

Materials



Paper


Dataset


Codes
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Citation

@article{zhang2024bjtt,
  title={BjTT: A Large-scale Multimodal Dataset for Traffic Prediction},
  author={Zhang, Chengyang and Zhang, Yong and Shao, Qitan and Feng, Jiangtao and Li, Bo and Lv, Yisheng and Piao, Xinglin and Yin, Baocai},
  journal={arXiv preprint arXiv:2403.05029},
  year={2024}
}
@article{zhang2023chattraffc,
  title={ChatTraffc: Text-to-Traffic Generation via Diffusion Model},
  author={Zhang, Chengyang and Zhang, Yong and Shao, Qitan and Li, Bo and Lv, Yisheng and Piao, Xinglin and Yin, Baocai},
  journal={arXiv preprint arXiv:2311.16203},
  year={2023}
}