Bayesian Particle Tracking of Traffic Flows

@article{Polson2018BayesianPT,
  title={Bayesian Particle Tracking of Traffic Flows},
  author={Nicholas Polson and V. Sokolov},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2018},
  volume={19},
  pages={345-356}
}
  • Nicholas Polson, V. Sokolov
  • Published 2018
  • Computer Science, Mathematics
  • IEEE Transactions on Intelligent Transportation Systems
  • We develop a Bayesian particle filter for tracking traffic flows that is capable of capturing non-linearities and discontinuities present in flow dynamics. Our model includes a hidden state variable that captures sudden regime shifts between traffic free flow, breakdown, and recovery. We develop an efficient particle learning algorithm for real time online inference of states and parameters. This requires a two-step approach, first resampling the current particles with a mixture predictive… CONTINUE READING
    11 Citations

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