Traffic congestion is one of the major challenges faced by the transportation industry. While this problem carries a high economical and environmental cost, the need for an efficient design of optimal paths for passengers in multilayer network infrastructures is imperative. We consider an approach based on optimal transport theory to route passengers preferably along layers that are more carbon efficient than the road, e.g. rails. By analyzing the impact of this choice on performance, we find that this approach reduces carbon emissions considerably, compared to shortest-path minimization. Similarly, we find that this approach distributes traffic more homogeneously thus alleviating the risk of traffic congestions. Our results shed light on the impact of distributing traffic flexibly across layers guided by optimal transport theory.
The physics of governance networks: critical transitions in contagion dynamics on multilayer adaptive networks with application to the sustainable use of renewable resources
Faculty of Maritime Studies and Transport, University of Ljubljana（卢布尔雅那大学海事研究与运输学院）；Department of Electronics-ICT, Faculty of Applied Engineering, University of Antwerp（安特卫普大学应用工程学院电子与信息技术系）；Department of Transport & Planning, Delft University of Technology（代尔夫特理工大学交通与规划系）
Spatial Heterogeneity, Scale, Data Character, and Sustainable Transport in the Big Data Era
鉴于大数据的出现，我倡导并主张从托布勒定律到缩放定律，从欧几里德几何到分形几何，从高斯统计到帕累托统计，更重要的是从笛卡尔的机械思维到亚历山大的有机思维的范式转变。分形几何属于分形的第三种定义-也就是说，如果比大事物更多的小事物的缩放多次重复出现，则集合或图案是分形的（Jiang and Yin 2014）-而不是分形的第二种定义，它需要尺度和细节之间的幂律（Mandelbrot 1982）。新的分形几何更倾向于“遵循规则，约束和偶然条件，这些都是不可避免地在现实世界中遇到的”（Alexander et al. 2012，p. 395），不仅用于理解复杂性，而且用于创建复杂或有生命的结构（Alexander 2002-2005）。这篇社论试图澄清为什么范式转变是必不可少的，并详细阐述了几个概念，包括空间异质性（缩放定律），规模（或规模的第四种含义），数据特征（相对于数据质量），以及大数据时代的可持续交通。
