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Dynamic transportation networks have been analyzed for years by means ofstatic graph-based indicators in order to study the temporal evolution ofrelevant network components, and to reveal complex dependencies that would notbe easily detected by a direct inspection of the data. This paper presents astate-of-the-art probabilistic latent network model to forecast multilayerdynamic graphs that are increasingly common in transportation and proposes acommunity-based extension to reduce the computational burden. Flexible timeseries analysis is obtained by modeling the probability of edges betweenvertices through latent Gaussian processes. The models and Bayesian inferenceare illustrated on a sample of 10-year data from four major airlines within theUS air transportation system. Results show how the estimated latent parametersfrom the models are related to the airline's connectivity dynamics, and theirability to project the multilayer graph into the future for out-of-sample fullnetwork forecasts, while stochastic blockmodeling allows for the identificationof relevant communities. Reliable network predictions would allow policy-makersto better understand the dynamics of the transport system, and help in theirplanning on e.g. route development, or the deployment of new regulations.

A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport