*3.3. Holistic UAM Network Approaches*

Despite vertiport networks serving a specific purpose such as providing alternative means of transport for commuter traffic or specifically operating in airport environment as airport shuttles, several contributions focus on a holistic development of a vertiport network. The overall goal is to provide a structured and generic process on how a vertiport network can be developed based on e.g., socio-demographic, local travel/commuting and city planning characteristics. According to [100], many U.S. cities of UAM interest are following a "wheel-and-spoke" design with interstate highways radiating out from the city center and circumferential concentric beltways connecting the suburbs. Therefore, the generalized model of vertiport placement proposes a UAM traffic network aligned to existing highway traffic configurations which can be adjusted to *every American metropolitan area* by customizing the size of the hexagon. Following this approach of a generic city model consisting of a hexagonal vertiport placement pattern, a UAM system of system network was developed by [101] enabling the analysis of a UAM network of seven vertiports in *Houston (U.S.)* and five vertiports in *Dallas Fort-Worth (U.S.)*.

Based on socio-demographic characteristics and expected developments for the year 2030 (used tool: SILO for modelling a synthetic population), an existing agent-based traffic simulation model (used tool: MATSim for trip assignment, MITO for generating travel

demand) is used and extended to determine UAM demand and potential modal share for the metropolitan area of *Munich (Germany)* [102]. Within this study, the vertiport was inserted as a black box being able to accept and release UAM traffic. Serving four different business cases (business, commuting, tourism, leisure), three level of vertiport archetypes are considered; a low density network (24 vertiports) covering large agglomerations, transportation hub and densely populated areas with large share of high income; a medium density network (74 vertiports) including main subway and suburban lines and employment centers; a high density network (130 vertiports) covering all relevant trips and target groups [103]. Moreover, number of vehicles, cruise speeds, processing times and ticket fairs are varied. Potential vertiport locations are determined in the course of several workshops with representatives of Munich Airport, city of Munich and Ingolstadt and the Upper Bavarian Chamber of Industry and Commerce. For the medium density network a total UAM mode share of 1% was predicted, whereas targeting for longer distances, the mode share prediction increased to 3 to 4% [102].

A collaborative simulation approach is proposed by [104], in order to analyze a UAM network inside the metropolitan area of *Hamburg (Germany)*. It follows the objective of defining low-fidelity analysis components such as demand, vertiport design, vertiport integration, routing, scheduling and setting them into relation in order to analyze interdependencies. The vertiport integration is based on published 3D building data, which is then used to select a vertiport location in the centroid of every quarter in Hamburg. This is being reconciled with the expected demand, airspace structure and resulting routes, and general restrictions like no-fly zones.

A 3D geographic information system map was derived from lidar data and used by [105] to determine the optimal vertiport location for the *Tampa Bay area (U.S.)*. Both, regulation constraints for eVTOL operations at vertiports and socio-demographic characteristics were additionally considered. The potential UAM demand is analyzed and the UAM mode share is evaluated based on allocation of user to vertiport, access- and egress-mode choices and the interaction between vertiports. Ref. [105] concludes, that UAM ride shares are small therefore congestion relief will be limited, but the passengers who choose UAM will experience substantial time savings. Inside the network design, trips fully conducted by UAM or ground transportation modes as well as multi-modal ride shares are feasible. The network optimization follows the objective to minimize generalized travel cost for all network users no matter what transport mode was chosen. It is seen, that with increasing number of vertiports the overall accessibility and UAM mode share increases. However, this is saturated choosing a vertiport network of 80 vertiports. The transfer time between ground based modes and UAM plays a decisive role, which leads into a drastic reduction in numbers of customers if the transfer time is increasing.
