  A Bayesian approach to predicting traffic flows at signalised intersections
is considered using the the INLA framework. INLA is a deterministic,
computationally efficient alternative to MCMC for estimating a posterior
distribution. It is designed for latent Gaussian models where the parameters
follow a joint Gaussian distribution. An assumption which naturally evolves
from an LGM is that of a Gaussian Markov Random Field (GMRF). It can be shown
that a traffic prediction model based in both space and time satisfies this
assumption, and as such the INLA algorithm provides accurate prediction when
space, time, and other relevant covariants are included in the model.
