## 7 Conclusions  
The proposed research provided a methodology for modelling mobility networks. The importance of the network design as well as its elements and options has been shown based on our results. Moreover, the following conclusions were reached: a generic air mobility management model as well as an ODAM management model were developed; the ODAM conceptual model was applied to a simulation model for electric aviation aircraft units within a network of vertiports with on-demand bookings. For this purpose, the customer request processing model and electric aviation cycle were applied. As a result, the system dynamics simulation provided an ideal framework to compare different scenarios. The simulation model analyzed the variance of factors such as maintenance policies and redistribution of aircraft units while considering given aircraft capacities, vertiports capacities, the distribution of charging times and battery lifespan, as well as distances and lead times between vertiport locations. The simulation case study provided an application with constrained capacity management of vertiport capacities in an electric air mobility network as well as insights into vertiport capacities for a given demand (i.e., which routes and circumstances had service problems and needed to be adjusted). The simulation results showed how high-performance maintenance management and daily redistributions of aircraft significantly improved the external service provision in quantity of passengers and in quality with less minutes per trip, as well as the aircraft on-ground time percentage. In conclusion, this proposed approach could increase travel demand satisfaction via time savings for passengers and operators as well as with a better utilization of assets.  
Future research areas may include:  
* An extension of the conceptual model for an integrated mobility concept with scalability.
* A detailed analysis of the factors of the simulation model.
* An application of the model for route scheduling and traffic management as well as for monitoring indicators for the continuous optimization of the network, balancing mobility needs against operational costs, and environmental impacts.
* An extension of the functionality of the simulation model including an economic model with price fluctuations, economic cycles, the evolution of shared mobility, population characteristics and demographics, the impact of global events such as COVID-19, increased flexibility of the distribution network, different regions and stations, and the mobility vehicle life cycle.  
* An application of the conceptual model methodology and the simulation model to a real-world mobility network for a region.  
This kind of model and assessment provide guidance for new network configurations as well as for the optimization of existing networks. For this reason, many public and private organizations and professionals, such as suppliers, operators, administration authorities, and users, may provide personalized data and customized details to obtain more accurate predictions and assessments.  
Conceptualization, P.W., M.G., and S.G.-G.; data curation, S.G.-G.; formal analysis, S.G.-G.; Investigation, P.W. and S.G.-G.; methodology, M.G. and S.G.-G.; resources, S.G.-G.; software, S.G.-G.; supervision, S.G.-G.; validation, P.W. and S.G.-G.; writing--original draft, S.G.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.