Estimating Traffic Density Using Transformer Decoders

    •  Wang, Y., Zhang, J., Nikovski, D., Kojima, T., "Estimating Traffic Density Using Transformer Decoders", International Workshop on Statistical Methods and Artificial Intelligence, March 2023.
      BibTeX TR2023-011 PDF
      • @inproceedings{Wang2023mar,
      • author = {Wang, Yinsong and Zhang, Jing and Nikovski, Daniel and Kojima, Takuro},
      • title = {Estimating Traffic Density Using Transformer Decoders},
      • booktitle = {International Workshop on Statistical Methods and Artificial Intelligence},
      • year = 2023,
      • month = mar,
      • url = {}
      • }
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  • Research Areas:

    Data Analytics, Machine Learning


We propose a combined particle-based density prediction model consisting of three components: trajectory prediction for existing particles, entering particle prediction, and iterative sampling. At initialization, the combined model takes in a set of trajectories for trajectory prediction and a sequence of observation vectors for entering particle prediction. Then, the iterative sampling module generates the density prediction for the next time instance. It will also sample a pool of particles and pass on their trajectories to the next trajectory prediction model for future density prediction.