Short-Term Urban Rail Passenger Flow Forecasting: A Dynamic Bayesian Network Approach

@article{Roos2016ShortTermUR,
  title={Short-Term Urban Rail Passenger Flow Forecasting: A Dynamic Bayesian Network Approach},
  author={J{\'e}r{\'e}my Roos and St{\'e}phane Bonnevay and G{\'e}rald Gavin},
  journal={2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  year={2016},
  pages={1034-1039}
}
We propose a dynamic Bayesian network approach to forecast the short-term passenger flows of the urban rail network of Paris. This approach can deal with the incompleteness of the data caused by failures or lack of collection systems. The structure of the model is based on the causal relationships between the adjacent flows and is designed to take into… CONTINUE READING