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

TL;DR

This paper introduces a probabilistic multilayer network model with community detection to forecast large, dynamic transportation graphs, demonstrated on US airline data, aiding policy and planning decisions.

Contribution

It presents a novel probabilistic latent network model with a community-based extension for efficient forecasting of multilayer transportation graphs.

Findings

Model accurately forecasts US airline network dynamics.

Latent parameters relate to connectivity changes.

Community detection identifies key airline groups.

Abstract

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 probabilistic 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.