Deep Learning on Traffic Prediction

1.Dataset

We collect and summarize available traffic prediction datasets.

Proceed as follows to download the public datasets used for the prediction task.

2.Experiment

We perform a comparative experimental study to evaluate different models, identifying the most effective component.

Proceed as follows to understand the configuration method of our experiment
Proceed as follows to understand the analysis of our experiment

3.Approches

We summarize some approaches to deep learning for traffic prediction and presented their public code in approaches.md

Furthermore, we do a taxonomy for existing approaches, describing their key design choices in our paper.

Key techniques of traffic prediction methods

4.Paper

Xueyan Yin, Genze Wu, Jinze Wei, Yanming Shen, Heng Qi, Baocai Yin, "Deep Learning on Traffic Prediction: Methods,Analysis and Future Directions",IEEE,2021.
If you find this code and dataset useful for your research, please cite our paper:

@ARTICLE{9352246,
  author={Yin, Xueyan and Wu, Genze and Wei, Jinze and Shen, Yanming and Qi, Heng and Yin, Baocai},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  title={Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions},
  year={2021},
  volume={},
  number={},
  pages={1-17},
  doi={10.1109/TITS.2021.3054840}}