Forecasting the evolution of fast-changing transportation networks using machine learning.

ABSTRACT: Transportation networks play a critical role in human mobility and the exchange of goods, but they are also the primary vehicles for the worldwide spread of infections, and account for a significant fraction of CO₂ emissions. We investigate the edge removal dynamics of two mature but fast-changing transportation networks: the Brazilian domestic bus transportation network and the U.S. domestic air transportation network. We use machine learning approaches to predict edge removal on a monthly time scale and find that models trained on data for a given month predict edge removals for the same month with high accuracy. For the air transportation network, we also find that models trained for a given month are still accurate for other months even in the presence of external shocks. We

SUBMITTER: Lei W 

PROVIDER: S-EPMC9307821 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature