The extension of the current urban transportation system utilising the third dimension by air taxi (AT) operations represents a potential solution for the congestion of metropolitan areas. A major asset for AT operations is the connection to existing airports enabling the access to multiple other transportation systems. This paper develops an analytical model for AT operations and their capacity impact on airports, exemplary for Hamburg airport. The model is developed, based on the results of a fast time simulation (FTS) considering multiple aspects, such as vehicle configuration and touchdown and lift-off areas (TLOF). Collectively, three integration methods were analysed, each of them impacting the conventional air traffic differently. The results show that an integration using the runway-system is not possible with five ATs per hour. Further methods allow an integration of up to 20 air taxis per hour. Additionally, an energy consumption analysis of the ATs is conducted. Finally, proposals are given for integrating ATs at an airport and further strategies to extend the analytical model. Through this work, a model to calculate and predict an AT’s influence on the airside capacity of an airport is designed. This is an important step for the practical implementation of AT operations at airports.
Efficiency, safety, feasibility, sustainability and affordability are among the key characteristics of future urban mobility. The project "HorizonUAM -Urban Air Mobility Research at the German Aerospace Center (DLR)" provides first answers to this vision by pooling existing competencies of individual institutes within DLR. HorizonUAM combines research about urban air mobility (UAM) vehicles, the corresponding infrastructure, the operation of UAM services, as well as public acceptance and market development of future urban air transportation. Competencies and current research topics including propulsion technologies, flight system technologies, communication and navigation go along in conjunction with the findings of modern flight guidance and airport technology techniques. The project analyses possible UAM market scenarios up to the year 2050 and assesses economic aspects such as the degree of vehicle utilization or cost-benefit potential via an overall system model. Furthermore, the system design for future air taxis is carried out on the basis of vehicle family concepts, onboard systems, aspects of safety and security as well as the certification of autonomy functions. The analysis of flight guidance concepts and the sequencing of air taxis at vertidromes is another central part of the project. Selected concepts for flight guidance, communication and navigation technology will also be demonstrated with drones in a scaled urban scenario. This paper gives an overview of the topics covered in the HorizonUAM project, running from mid-2020 to mid-2023, as well as an early progress report.
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Airport management plays a key role in the air traffic system. Introducing resources at the right time can minimize the effects of disruptions, reduce delays, and save costs as well as optimize the carbon footprint of the airport. Efficient decision-making is a challenge due to the uncertainty of the upcoming events and the results of the applied countermeasures. So-called ‘what-if’ systems are under research to support the decision-makers. These systems consist of a user interface, a case management system, and a prediction engine. Within this paper, we evaluate different types of prediction engines (flow, event, and motion models) that can be used for airport management what-if systems by comparing them in terms of accuracy and calculation speed. Hence, two different operational situations are examined to evaluate the performance of the prediction engines. The comparison shows that accuracy and calculation speed are opposed. The flow model has the lowest accuracy but the shortest calculation time and the motion model has the highest accuracy but the longest calculation time. The event model lies between the other two models. The acceptable accuracy of a prediction tool is strongly dependent on the respective airport, whereas the calculation time is strongly dependent on the available decision time. Regarding airport management, this means that the selection of a prediction engine has to be made in dependence of the airport and the decision processes. The results show the advantages and disadvantages of each prediction engine and provide a first quantification by which a selection for what-if systems can happen.
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