Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA.
Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.
Nat Commun. 2022 Jul 22;13(1):4252. doi: 10.1038/s41467-022-31911-2.
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 CO 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 take advantage of this approach to forecast the impact of a hypothetical dramatic reduction in the scale of the U.S. air transportation network as a result of policies to reduce CO emissions. Our forecasting approach could be helpful in building scenarios for planning future infrastructure.
交通网络在人类流动和货物交换中起着至关重要的作用，但它们也是全球传染病传播的主要载体，占 CO 排放量的很大一部分。我们研究了两个成熟但变化迅速的交通网络的边去除动态：巴西国内公共汽车运输网络和美国国内航空运输网络。我们使用机器学习方法来预测每月的边去除动态，并发现基于给定月份数据训练的模型可以高精度地预测同一月份的边去除。对于航空运输网络，我们还发现，即使存在外部冲击，为给定月份训练的模型对于其他月份仍然准确。我们利用这种方法来预测由于减少 CO 排放的政策而导致美国航空运输网络规模假设性大幅减少的影响。我们的预测方法有助于为规划未来基础设施构建情景。
