Stochastic Demand Dynamic Traffic Models Using Generalized Beta-Gaussian Bayesian Networks

@article{Castillo2012StochasticDD,
  title={Stochastic Demand Dynamic Traffic Models Using Generalized Beta-Gaussian Bayesian Networks},
  author={Enrique F. Castillo and Mar{\'i}a Nogal and Jos{\'e} Mar{\'i}a Men{\'e}ndez and Santos S{\'a}nchez-Cambronero and Pilar Jim{\'e}nez},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2012},
  volume={13},
  pages={565-581}
}
  • Enrique F. Castillo, María Nogal, +2 authors Pilar Jiménez
  • Published in
    IEEE Transactions on…
    2012
  • Computer Science, Mathematics
  • A stochastic demand dynamic traffic model is presented to predict some traffic variables, such as link travel times, link flows, or link densities, and their time evolution in real networks. The model considers that the variables are generalized beta variables such that when they are marginally transformed to standard normal, they become multivariate normal. This gives sufficient degrees of freedom to reproduce (approximate) the considered variables at a discrete set of time-location pairs. Two… CONTINUE READING

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