Transport and Epidemic Networks: Graphs, Optimization and Simulation (TENGOS)

持续的COVID-19大流行表明，在这种情况下维护关键基础设施对于保护基本服务和将经济损害降至最低至关重要。因此，当局在保护基础设施的功能和限制流行病传播之间稳步平衡。交通运输网络对于人员和货物的流动以保持经济和日常供应的运作仍然至关重要，在最佳功能和减少感染之间找到适当的平衡仍然是一项固有的复杂任务;特别是，由于交通运输网络仍然是感染的主要焦点-特别是最近对可持续和共享交通的雄心壮志，重点是公共交通和拼车。在这方面，有两个问题至关重要。在战术层面上，关键是要了解操作模式和限制的组合允许我们限制感染，以允许安全操作。在战略层面，运输网络的设计仍然是一个核心挑战。交通运输网络应允许在正常时期可持续地运营，但在制定了疫情缓解战略的极端情况下，也应允许稳健和有复原力的运营。为了提高效率，网络不应依赖并行基础设施或冗余能力。为了回答这些问题，我们通过耦合流行病网络和交通网络来研究多层网络动力学，其中每个在交通网络中旅行的人也是流行病网络的一部分。为了进行整体评估和决策支持，我们结合了来自三个学科的联合收割机方法：流行病建模，运输优化和运输模拟。具体而言，我们结合联合收割机两个动力系统建模流行病网络与基于流量的交通网络模型。我们开发基于优化的算法，允许设计和运行强大的交通网络在流行病期间，我们研究减少多层运输流行病动力学通过微分方程，我们基准我们的结果对基于代理的运输模拟。这种多方面的方法使我们能够量化交通网络和运营理念变化的影响。我们还使用这种算法环境来设计和测试新的交通网络和潜在的疫情缓解策略，以开发新常态下高效安全的交通系统。

The ongoing COVID-19 pandemic shows that maintaining critical infrastructure during such a scenario is imperative to preserve essential basic services and to limit economic harm to a minimum. Accordingly, authorities steadily balance between preserving the functionality of infrastructure and confining epidemic spreading. For transportation networks, which remain crucial for the movement of people and goods to keep the economy and daily supply operational, finding the right balance between optimal functionality and reducing infections remains an inherently complex task; especially, as transportation networks remain major foci of infection -- in particular with recent ambitions towards sustainable and shared transportation, which focus on public transport and ride pooling. In this context, two questions are central. At tactical level, it is crucial to understand which combination of operating modes and restrictions allow us to limit infections in order to allow for safe operations. At strategic level, the design of transportation networks remains a central challenge. Transport networks shall allow for sustainable operations during normal times but also for robust and resilient operations in extreme situations when epidemic mitigation strategies are in place. To be efficient, the networks should not rely on parallel infrastructure or redundant capacities. To answer these questions, we study multilayer network dynamics by coupling epidemic networks and transportation networks, where each person that travels in a transportation network is also part of an epidemic network. To allow for a holistic assessment and decision support, we combine methodologies from three disciplines: epidemic modeling, transport optimization, and transport simulation. Specifically, we combine two dynamical systems modelling epidemic networks in combination with flow-based transportation network models. We develop optimization-based algorithms that allow to design and operate robust transportation networks during epidemics, we study reduced multilayer transport-epidemic dynamics via differential equations, and we benchmark our results against agent-based transport simulations. This multifaceted approach allows us to quantify the impact of changes in the transportation networks and operational concepts. We also use this algorithmic environment to design and test new transport networks and potential epidemic mitigation strategies to develop efficient and safe transportation systems for a new normal.