The design or the optimization of transport systems is a difficult task. This is especially true in the case of the introduction of new transport modes in an existing system. The main reason is, that even small additions and changes result in the emergence of new travel patterns, likely resulting in an adaptation of the travel behavior of multiple other agents in the system. Here we consider the optimization of future Urban Air Mobility services under consideration of effects induced by the new mode to an existing system. We tackle this problem through a bi-level network design approach, in which the discrete decisions of the network design planner are optimized based on the evaluated dynamic demand of the user's mode choices. We solve the activity-based network design problem (AB-NDP) using a Genetic Algorithm on a multi-objective optimization problem while evaluating the dynamic demand with the large-scale Multi-Agent Transport Simulation (MATSim) framework. The proposed bi-level approach is compared against the results of a coverage approach using a static demand method. The bi-level study shows better results for expected UAM demand and total travel time savings across the transportation system. Due to its generic character, the demonstrated utilization of a bi-level method is applicable to other mobility service design questions and to other regions.

翻译：交通系统的设计或优化是一项艰巨任务，尤其是在现有系统中引入新型交通方式时。主要原因在于，即使是微小的补充与改变，也可能催生新的出行模式，进而导致系统中其他多重主体的出行行为产生适应性调整。本文在考虑新型交通方式对既有系统产生影响的前提下，探讨未来城市空中交通服务的优化问题。我们采用双层网络设计方法予以解决：基于用户出行方式选择的动态需求评估结果，对网络设计规划者的离散决策进行优化。我们利用遗传算法求解多目标优化问题中的基于活动行为的网络设计问题，同时借助大规模多智能体交通仿真框架评估动态需求。该双层方法的结果与采用静态需求方法的覆盖式方法进行了对比分析。研究表明，双层方法在预期城市空中交通需求及整个交通系统总出行时间节省方面呈现更优成效。鉴于其通用性，本文展示的双层方法应用可推广至其他出行服务设计问题及不同区域。