Accurate freeway travel time prediction with state-space neural networks under missing data

@article{Lint2005AccurateFT,
  title={Accurate freeway travel time prediction with state-space neural networks under missing data},
  author={J. V. Lint and S. Hoogendoorn and H. Zuylen},
  journal={Transportation Research Part C-emerging Technologies},
  year={2005},
  volume={13},
  pages={347-369}
}
  • J. V. Lint, S. Hoogendoorn, H. Zuylen
  • Published 2005
  • Engineering
  • Transportation Research Part C-emerging Technologies
  • Accuracy and robustness with respect to missing or corrupt input data are two key characteristics for any travel time prediction model that is to be applied in a real-time environment (e.g. for display on variable message signs on freeways). This article proposes a freeway travel time prediction framework that exhibits both qualities. The framework exploits a recurrent neural network topology, the so-called state-space neural network (SSNN), with preprocessing strategies based on imputation… CONTINUE READING

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