Dynamic transportation networks have been analyzed for years by means of
static graph-based indicators in order to study the temporal evolution of
relevant network components, and to reveal complex dependencies that would not
be easily detected by a direct inspection of the data. This paper presents a
state-of-the-art latent network model to forecast multilayer dynamic graphs
that are increasingly common in transportation and proposes a community-based
extension to reduce the computational burden. Flexible time series analysis is
obtained by modeling the probability of edges between vertices through latent
Gaussian processes. The models and Bayesian inference are illustrated on a
sample of 10-year data from four major airlines within the US air
transportation system. Results show how the estimated latent parameters from
the models are related to the airline's connectivity dynamics, and their
ability to project the multilayer graph into the future for out-of-sample full
network forecasts, while stochastic blockmodeling allows for the identification
of relevant communities. Reliable network predictions would allow policy-makers
to better understand the dynamics of the transport system, and help in their
planning on e.g. route development, or the deployment of new regulations
