Urban Network Travel Time Prediction Based on a Probabilistic Principal Component Analysis Model of Probe Data

@article{Jenelius2018UrbanNT,
  title={Urban Network Travel Time Prediction Based on a Probabilistic Principal Component Analysis Model of Probe Data},
  author={Erik Jenelius and Haris N. Koutsopoulos},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2018},
  volume={19},
  pages={436-445}
}
This paper proposes a network travel time prediction methodology based on probe data. The model is intended as a tool for traffic management, trip planning, and online vehicle routing, and is designed to be efficient and scalable in calibration and real-time prediction; flexible to changes in network, data, and model extensions; and robust against noisy and missing data. A multivariate probabilistic principal component analysis (PPCA) model is proposed. Spatio-temporal correlations are inferred… CONTINUE READING

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