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，当局在维护基础设施的功能和限制流行病传播之间取得了稳定的平衡。交通网络对于人员和货物的流动至关重要，以保持经济和日常供应的运行，在最佳功能和减少感染之间找到适当的平衡仍然是一项本质上复杂的任务；特别是，由于交通网络仍然是主要的感染焦点，特别是最近对可持续和共享交通的雄心，重点是公共交通和拼车。在此背景下，有两个问题至关重要。在战术层面，了解哪种操作模式和限制措施的组合使我们能够限制感染，从而实现安全操作是至关重要的。在战略层面，交通网络的设计仍然是一个核心挑战。运输网络应能在正常时期实现可持续运营，但在制定了流行病缓解战略的极端情况下，也能实现稳健和有弹性的运营。为了提高效率，网络不应该依赖于并行基础设施或冗余容量。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，其中在交通网络中旅行的每个人也是流行病网络的一部分。为了全面评估和决策支持，我们结合了三个学科的方法：流行病建模、运输优化和运输模拟。具体来说，我们将两个动态系统建模流行病网络与基于流的运输网络模型相结合。我们开发了基于优化的算法，允许在流行病期间设计和运行稳健的运输网络，我们通过微分方程研究了减少的多层运输-流行病动力学，并将我们的结果与基于agent的运输模拟进行了基准测试。这种多方面的方法使我们能够量化交通网络和运营概念变化的影响。我们还使用这个算法环境来设计和测试新的交通网络和潜在的流行病缓解策略，为新常态开发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.