Abstract
Urban Air Mobility has the potential to substantially reduce travel times in some cases of urban-related transportation. Travel time savings strongly depend on fast processing at vertiports, which presents a key challenge considering demand levels’ vertiports would experience when becoming an established mode of transport. This article sheds light on the passenger throughput vertiport airfields can manage and how the operations are sensitive to changes. One main contribution of this article is the introduction of hourly passenger throughput per area as a performance indicator that allows to compare vertiports of different sizes. VoloCity is studied as a reference vehicle and the resulting space requirement of the carefully specified baseline scenario is 188 square-meters per passenger per hour. A total of 13 prominent eVTOL designs are included in the study from which the current design space between maximum vehicle dimension and number of seats is deducted. The study shows that vehicles with a small maximum dimension yield the highest passenger throughput capacity. CityAirbus performs best (46.3 m2/PAX/h) with a diameter of 7.92 m and Archer Maker performs worst (221 m2/PAX/h) with a diameter of 12.2 m. How the performance indicators can be used as rules-of-thumb in the first-order estimations of vertiport throughput capacity or space requirement is described by means of illustrative examples. The insights presented in this paper might be useful for researches, vehicle developers, and municipalities alike.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
1 Introduction
Urban Air Mobility (UAM) is an emerging transportation concept that has the potential to enrich the existing transport system through a new mode with the particular advantage of reducing travel times. The introduction of UAM faces several hurdles [1], among which infrastructure has been identified as a key issue by NASA [2], DLR [3], and MIT [4, 5]. Next to the aircraft-specific issues around Electric Vertical Take-Off and Landing Vehicles (eVTOL) [6,7,8,9], there are various hurdles to overcome, such as air traffic management [10,11,12,13,14,15,16,17,18], noise [19,20,21,22], or safety and certification [23,24,25]. Many of these issues are already being addressed as documented by various literature reviews and white papers [26,27,28,29], while the question of ground infrastructure only finds secondary attention. In particular, the locating and throughput capacity of vertiports has recently been identified as a significant research gap [30]. Reference [31] established that the comparatively small volume of research available on vertiports is centrally focused on airspace operations and design; throughput capacity and ground operations have received less attention.
This paper wants to fill the research gap between the known importance of UAM ground infrastructure and the lack of insight into vertiport throughput capacity. For this purpose, an existing Vertiport Sizing Method (VSM) [32] (see Sect. 2) is applied first to a baseline scenario using VoloCity as reference vehicle and various sizes of vertiports (see Sect. 3) and second to a study contrasting prominent eVTOL designs (see Sect. 4). Performance indicators are defined to measure passenger throughput (see Sect. 3.2).
At the heart of all considerations is the idea of hourly throughput per area. Space in cities is a valuable commodity and therefore land use a relevant performance indicator. This approach has been used in the past for other modes of transport, showing that rail needs less space per passenger than cars [33]. Taking Germany as an example where 5% of the total land area is dedicated to transport [34], one can find the following space demand per daily passenger: 65 m2 for cars, 32 m2 for rail, and 620 m2 for airplanes (the numbers were calculated by comparing statistics from the German Federal Statistical Office for land use [35] and passenger transport [36]). A direct comparison of these existing space demands with the estimated space demands for eVTOLs can be found in Appendix A.5.
2 Literature review and previous work
In a previous publication, a Mixed-Integer Programming (MIP) approach for sizing and designing vertiports has been presented [32]. The present work applies this MIP approach to analyze vertiport airfield passenger throughput capacities and expands the preliminary studies of [37]. Further publications that should find mention are Vascik’s ground-breaking analysis of vertiport capacity envelopes, which served as inspiration for the MIP approach [38]. Another important work is by Zelinski, who looked at vertiport design in a holistic sense, considering among other things weather impact and vertiport topologies [39]. Reference [40] studied vertiport configurations for the Cologne-Bonn airport and determines a capacity of 9.6 aircraft movements per hour for the specific use case. Reference [41] developed a method to estimate the cost of various vertiport layouts and state an average of 420 m2 per hourly vehicle throughput (for a direct comparison with the results of this article, see Appendix A.5). A patent for dynamic vertiport configurations was published by Ref. [42] and efforts to craft an ISO standard for vertiports is under way [43]. Further studies exploring different vertiport layouts and sizes were published by Deloitte [44], Lilium [45], and McKinsey [46].
Here, the VSM published by Preis [32] will be re-iterated briefly. The VSM uses MIP in a branch-and-bound fashion with a utility function of maximizing hourly vehicle or passenger throughput. Vehicle throughput is defined as the number of vehicles being able to complete the following process within 1 h: (1) approach of a vehicle from the airspace and landing on a pad, (2) taxiing to a gate, (3) turnaround at the gate including passenger boarding and de-boarding, (4) taxiing back to a pad, and (5) take-off and departure into the airspace (see Fig. 1). The passenger throughput, which will be the main unit of measurement in this paper, is formed by multiplying the vehicle throughput with the number of seats. Beneath this definition lies the assumption of a load factor of 1.0. Based on the size and shape of a given surface area, the optimal vertiport airfield layout is determined including the number of pads and gates, a suggestion for the topology, and the maximum possible hourly throughput. There are four topologies, which are compared for each scenario: single-pad, satellite, linear, and pier (see Fig. 2). Each topology follows a list of geometric rules, which are detailed by Hack Vazquez [47]. One geometric rule forces pads to be lined up along the rim of the surface to ensure obstacle free approach and departure paths.
Assumptions for the pads are taken from the recently published Engineering Brief No. 105, Vertiport Design by FAA [48]. The report states that “in future guidance, parking and taxi-ways guidance will be included; if necessary in the interim, […] vertiport design should follow taxiway guidance in AC 150/5390-2”. Therefore, the assumptions for the dimensions of gates and taxi-ways, including their safety zones, are derived from the “Heliport Design Guidelines” published by the Advisory Circle 150/5390-2C of the FAA [49]. Similar considerations are currently under way from EASA with a Means of Compliance SC-VTOL [50] while giving less details for airfield layouts and thereby yielding less utility for my article. A systematic discussion of historic and present vertiport design guidelines can be found in Ref. [31].
3 Baseline scenario and throughput performance
3.1 Definition baseline scenario
The operational parameters required by the VSM are shown in Table 1 and the values are chosen according to previous vertiport parameter specification [51] and aggregation [52]. The parameter values were determined through literature review and an expert interview series (n = 17) with participants from academia and industry. Approach & landing and take-off & departure of vehicles are each aggregated into one parameter and correspond to the time a pad is occupied with the respective operation (see Appendix A.1 for details on the value aggregation). As reference vehicle, the VoloCity from Volocopter was chosen (see Fig. 3) with two seats and a maximum dimension of 11.3 m [53]. The taxi-mode is “hovering close to the surface” with engines being shut-off after touch-down at the gate. For simultaneous operations of pads, the distance between their two Final Approach and Take-Off Area (FATO) must be at least 200 ft according to the heliport design guidelines by FAA [49]. All parameter values are listed in Table 1.
The baseline scenario has a turnaround time of 30 min, which might entail charging, battery swapping, or minor maintenance activities. It is assumed that vehicle-related turnaround and passenger boarding can happen simultaneously at the gate; the longer time of both determines to overall turnaround time. The total boarding time is the boarding and de-boarding time of one passenger multiplied by the number of seats. The load factor of each vehicle is assumed to be 1.0: during each turnaround, all passengers de-board the aircraft and new passengers board the aircraft until all seats are occupied. The length of the passenger-related turnaround has corresponding duration.
As illustration of the VSM process, Fig. 4 is included. Using the parameters outlined in Table 1 and giving an exemplary area of 6100 m2 with a rectangular shape and an aspect ratio of 1:2, a passenger throughput of 32 per hour is possible. The optimal ratio of gates to pads and the spatial layout is computed for the four different topologies (single-pad, satellite, linear, and pier) as described in Sect. 2. Finally, the highest performing topology is selected, which is the linear topology in this case using nine gates per pad.
3.2 Definition of throughput performance indicator
Scenarios will be measured and compared based on the performance indicator of “hourly passenger throughput per area” tppax and its reciprocal “area demand per hourly passenger throughput” tpA (see Eqs. 1 and 2, respectively). First, vehicle throughput TPveh is defined according to the chain of processes shown in Fig. 1: “vehicle throughput of one per hour” means that the listed operations (arrival, taxi to gate, turnaround including boarding, taxi to pad, and departure) can take place once within 1 h on the given vertiport airfield. The passenger throughput per hour TPpax is TPveh multiplied by the number of seats n in the vehicle. It should be explicitly mentioned that the number of seats (including the pilot seat) and not the number of passengers are counted to allow for comparison between piloted and autonomous eVTOL designs. In other words, the implicit assumption is autonomous eVTOLs as the number of seats and number of passengers are equated. Next, TPpax is divided by the area of the vertiport airfield A. This yields the performance indicator “hourly passenger throughput per area” tppax, as shown in Eq. 1. This indicator allows for direct comparison between vehicles and different sizes of vertiports and will be used throughout this paper. To make the performance indicator more intuitive, the reciprocal of tppax is introduced alongside: “area demand per hourly passenger throughput” tpA (see Eq. 2)
How the performance indicators tppax and tpA are formed is illustrated in Fig. 5. The passenger throughput per hour is shown on the left side displaying the area, throughput, aspect ratio of the area, and the best-performing topology for each scenario. Hourly passenger throughput per area tppax normalizes the throughput against the area and the area demand per hourly passenger throughput tpA normalizes the area against the throughput. Finally, both indicators are condensed into a single boxplot as shown on the right. The interpretation and application of the performance indicators are discussed in Sect. 3.3 and Appendix, respectively.
3.3 Evaluation of baseline scenario
The baseline scenario as defined in Sect. 3.1 was simulated with the VoloCity as reference vehicle for areas from 100 to 10,000 m2. The step size between areas is 100 m2 and each area was considered in three variations as rectangles with aspect ratios 1:1, 1:2, and 1:3, yielding a total of 300 scenarios. Expressing the results in a rule-of-thumb, the median value of all scenarios is calculated for both throughput performance indicators. For the baseline scenario, the values are tppax = 0.0053 PAX/h/m2 and tpA = 188 m2/PAX/h. The process of forming these rules-of-thumb is illustrated in Fig. 5 (the same process will be repeated for various prominent eVTOLs in Sect. 4.2 and is visualized for eHang 216 and Joby S4 in Appendix A.2). As the nature of vertiport airfield elements (pads, gates, etc.) is discrete (one cannot add half a pad), so will the results also be discrete. The discrete characteristic becomes apparent in the following plots where the norm is value—“jumps” instead of continuity.
Further, the gate-to-pad ratio will be discussed, as shown in Fig. 6, and how size and shape of an area influence both the optimal gate-to-pad ratio and the ideal topology. While single-pad topologies are best suited for small areas, there is a near-even split for medium and large areas between satellite and linear topologies. Pier topologies are at no point optimal within the scope of the study (in related studies, it was observed that pier topologies grow in importance for short turnaround times and larger areas). Scenarios that favor satellite topologies seem to prefer a gate-to-pad ratio of 4 or 5. Scenarios that favor linear topologies can range from 2 to 9 gates per pad. There are two separate trends visible for the linear topology, which can be explained by a particular geometric rule: if the size and shape of an area allow for it, a linear topology can add a second row of pads and gates; the jump from one to two rows explains the two separate groups of linear topologies. Other than that, no clear trends are observable, which indicates the uniqueness of each scenario.
In a second step, the charging time of the baseline scenario of 30 min is varied to understand the impact of the turnaround time at the gate. The variations are no charging time (which means that only passenger boarding takes place at the gates) and 60 min charging time. The former can be interpreted as a touch-and-go “verti-stop” in a strongly space constrained inner city environment, and the latter as a “verti-hub” outside the city with extensive parking and maintenance facilities. Hourly passenger throughput per area is listed in Table 2 and area demand per hourly passenger throughput is listed in Table 3. How these numbers can be applied to the first-order estimations of either throughput capacity or area demand is exemplified in Appendix A.3.
4 Study of prominent eVTOL designs
There are many ongoing eVTOL development projects of which the most mature and promising vehicles will be compared according to their operational performance in this section. The performance indicators tppax and tpA will be used as explained in Sect. 3.2. A total of 13 vehicles were chosen, which are either prominent in the scientific literature or are close to receiving flight certification. For an extensive treatment of eVTOL development projects, please refer to Refs. [6,7,8,9].
4.1 Vehicle dimensions and seats
eVTOLs come in many different configurations: the three most prominent are multicopter, lift + cruise, and tilt-wing/prop configurations. Thirteen vehicles, including their dimensions and number of seats, are shown Table 4. Taking the seat per maximum dimension ratio as a measure of operational performance, the CityAirbus and the Lilium Jet perform best and Archer Maker, Wisk Cora, and the previously considered VoloCity perform worst. For reasons of comparability between autonomous and (human) piloted configurations, the pilot seat is counted among the total seats.
The design space derived from the collection of eVTOLs is shown in Fig. 7. The lower end is marked by a straight line going through the coordinate origin with a slope of 2 m per seat. The left end is marked by various vehicles with two seats; among the prominent designs, no single-seat vehicle was found. The upper end is marked by a straight line cutting through 13 m maximum dimension at two seats with an upward slope of 1 m per seat. The largest vehicles are under 16 m maximum dimension.
Next to the vehicle characteristics, the dimensions of the airfield elements (pads, gates, and taxi-ways) are driving the vertiport layout and throughput. The dimensions according to FAA heliport guidelines [49] are discussed in Appendix A.4.
4.2 Prominent vehicle study results
The study from Sect. 3.1 which is based on the VoloCity was executed analogous to the baseline scenario for all 13 vehicles listed in Table 4. It should be mentioned that none of the mentioned eVTOLs is certified for passenger transport, yet. How realistic the concepts are is not included in the study (except for the a prior focus on “prominent” eVTOL designs) and the certification process will weed out unfeasible vehicles. The design space presented in this paper and the results might therefore shift as time progresses. The results of the prominent eVTOL design study will be visualized and discussed in the following.
A linear approximation of the passenger throughput per hour for areas between 100 and 10,000 m2 and all 13 eVTOLs can be seen in Fig. 8. The throughput performance indicators tppax and tpA (see Sect. 3.2 for the definition of the performance indicators) are then visualized in Figs. 9 and 10 and contrasted in Table 5. Further, the standard deviations for both performance indicators, σpax and σA, are listed in the table to indicate the level of statistical reliability. It can be seen that CityAirbus, eHang 216, and Vahana perform best while also being the smallest vehicles. At this point, it needs to be emphasized that passenger throughput (not vehicle throughput) is used as performance indicator. With this in mind, it appears that the penalty of reduced throughput for a larger vehicle dimension outweighs the benefit of placing more seats in the same vehicle. Therefore, it can be inferred that small vehicles—independent of their small number of seats—create the highest throughput capacity. This effect can be traced back to the following: when operating small eVTOLs, the footprint of pads and gates is also small wherefore more pads and gates can be placed on the same area creating higher throughput capacity. The increase in operational complexity, which could be a limiting factor, is not accounted for in the present study. This insight is somewhat counter-intuitive, as aviation tends to employ larger aircraft with more seats where high throughput is demanded.
The presented performance indicators “hourly passenger throughput per area” and “area demand per hourly passenger throughput” allow for direct comparison of existing eVTOL designs—which, admittedly, could be a decisive issue. Therefore, two cautionary remarks about the interpretation of the results should be provided. First, the results depend strongly on two numbers: the vehicle dimension and number of seats. These numbers were, for the most part, taken directly from the manufacturer’s websites and correspond to what the companies communicate with the public. Now, if eVTOL designs change, or the published numbers do not match the actual designs, the results would also change. Second, the throughput capacity on the vertiport is only one of many possible performance indicators to judge eVTOLs. Often used performance metrics are, for examples, speed, range, and payload. Yet, because space in inner city environments will be costly, the throughput capacity is an important metric to assess eVTOLs. A combined assessment of performance metrics will be published in a follow-up article as described in Sect. 6.
In Appendix, examples are given how tppax and tpA can be applied as rules-of-thumb to do the first-order estimations for vertiport sizing. tppax can be used to estimate the possible hourly passenger throughput on a given area and tpA can be used to determine the space requirement for a desired throughput capacity. Further, in Appendix A.5, the space requirements presented in Table 5 are compared to other modes of transport, such as rail, car, and commercial aviation.
5 Summary
Ground infrastructure is an essential part of the emerging transportation system of UAM, which has received only secondary attention so far. In particular, the throughput capacities of vertiports are a research gap this article attempts to address. An existing MIP approach (see [32] for the method and [37] for preliminary studies) is applied to a range of vertiport scenarios to better understand throughput capacities and sensitivities. The approach considers four topologies to compute the optimal vertiport airfield layout for each scenario: single-pad, satellite, linear, and pier. Vertiport airfield areas from 100 to 10,000 m2 are considered with VoloCity as the reference vehicle [53]. A baseline scenario is specified according to Preis et al. [51] with a turnaround time of 30 min at the gate. The summary of the vertiport sizing method and parameter values can be found in Sects. 2 and 3.1, respectively.
Next, the performance indicators “hourly passenger throughput per area” tppax and “area demand per hourly passenger throughput” tpA are introduced (see Sect. 3.2). These allow for direct comparison of operational performance of different scenarios and vertiport sizes. The baseline case of the VoloCity results in tppax = 0.0053 PAX/h/m2 and tpA = 188 m2/PAX/h. How these performance indicators, gate-to-pad ratios, and the choice of topologies varies is discussed in Sect. 3.3. In a second step, 13 prominent eVTOLs are investigated, and their maximum vehicle dimension and the number of seats is listed including a description of the deducted eVTOL design space (see Sect. 4.1). The consecutive studies presented in Sect. 4.2 showed that small vehicles (CityAirbus, eHang 216, and Vahana) perform best according to the above-defined indicators. The best performer is CityAirbus with a space requirement of 46.3 m2/PAX/h and the worst performer is Archer Maker with a space requirement of 221 m2/PAX/h. A comparison to other modes of transport is given in Appendix A.5.
The established insights can help three groups of people in the broader context of UAM. First, other researchers in academia can benefit from applying the rules-of-thumb for passenger throughput as input in their studies, in particular for making realistic vertiport capacity constraint assumptions in UAM demand studies. Second, vehicle developers can use the presented analysis to understand the operational performance of their own vehicle both individually and in comparison with other vehicles. Beneficial operational environments are highlighted for different types of vehicles. Third, cities or communities can use the results as the first-order estimations and gain a feeling for the possible throughputs and space requirements a potential UAM service would have in their region.
6 Limitations and future work
The limitations of this article lie with the assumptions on one hand and with the interpretation of the results on the other hand. The following assumptions should find mention: first, all scenarios are based on a homogenous single-vehicle-type fleet. Vertiport design for mixed fleets would need to follow the largest vehicle type and thereby put smaller vehicles at a relative disadvantage. How this would impact vertiport airfield design and throughput capacity should be subject to further research. Second, the load factor of vehicles in Sect. 2 is assumed to be 1.0, which will most likely be lower in real-life operations. It can be expected that vehicles with less seats would benefit from this type of refined analysis as it is easier to pool fewer passengers and thereby more likely to fill up, e.g., two seats instead of five seats. Third, the feasibility of eVTOL designs was not accounted for except for the selection criterion of “prominent” designs (see Sect. 4.2). Once first eVTOLs have been certified, it is recommended to revisit the deducted design space and adjust it if needed.
Interpreting the results in a meaningful way is limited by two aspects: first, the FAA vertiport design guidelines are subject to ongoing change as discussed in Sect. 2 and new guidelines might impact the significance of presented results. Second, the assessment of operational performance in terms of hourly passenger throughput per area appears to take on a wide range of possible values. This variation can be explained by the discrete nature of vertiport layouts (see Sect. 3.3). It is therefore advised to express passenger throughput performance in the form of a value-range instead of a single value. These limitations could inform future research; two concrete publications are already planned as described in the following.
A planned publication will address the question of how passenger throughput capacity correlates with other performance indicators. Among these indicators will be the classical aircraft triad of speed, range, and payload, but also other ground operations-related indicators such as disk loading and rotor tip speed as indicators of noise. The goal will be to develop a holistic framework of performance assessment for eVTOLs with the throughput capacity as particular contribution from the authors.
Abbreviations
- eVTOL:
-
Electric vertical take-off and landing vehicle
- FATO:
-
Final approach and take-off area
- MIP:
-
Mixed-integer programming
- TLOF:
-
Touch down and lift-off area
- UAM:
-
Urban air mobility
- VSM:
-
Vertiport sizing method
- A :
-
Area of vertiport airfield
- n :
-
Number of seats in eVTOL
- σ pax :
-
Standard deviation of hourly passenger throughput per area
- σ A :
-
Standard deviation of area demand per hourly passenger throughput
- TP veh :
-
Vehicle throughput per hour
- TP pax :
-
Passenger throughput per hour
- tp pax :
-
Hourly passenger throughput per area
- tp A :
-
Area demand per hourly passenger throughput
References
Vascik, P.D.: Systems analysis of urban air mobility operational scaling. Ph.D., Department of Aeronautics & Astrophysics, Massachusetts Institute of Technology (MIT), Cambridge. https://dspace.mit.edu/handle/1721.1/128057 (2020). Accessed 17 Dec 2022
National Aeronautics and Space Administration (NASA), Ed.: UAM Vision Concept of Operations (ConOps) UAM Maturity Level (UML) 4: Version 1.0. https://ntrs.nasa.gov/citations/20205011091 (2020) Accessed 13 Jul 2022
Schuchardt, B.I., et al.: Urban air mobility research at the DLR German Aerospace Center—getting the HorizonUAM project started. In: AIAA Aviation 2021 Forum, virtual event, p. 361. https://arc.aiaa.org/doi/abs/10.2514/6.2021-3197 (2021) Accessed 13 Jul 2022
Vascik, P.D. Hansman, J.R.: Scaling constraints for urban air mobility operations: air traffic control, ground infrastructure, and noise. In: 18th AIAA Aviation Technology, Integration, and Operations Conference 2018, Atlanta. https://arc.aiaa.org/doi/10.2514/6.2018-3849 (2018) Accessed 18 Dec 2022
Vascik, P.D., Hansman, J.R.: Constraint identification in on-demand mobility for aviation through an exploratory case study of Los Angeles. In: 17th AIAA Aviation Technology, Integration, and Operations Conference, Denver. https://arc.aiaa.org/doi/10.2514/6.2017-3083 (2017) Accessed 18 Dec 2022
Shamiyeh, M., Bijewitz, J., Hornung, M.: A review of recent personal air vehicle concepts. In: 6th CEAS Conference. https://www.icas.org/ICAS_ARCHIVE/ICAS2018/data/papers/ICAS2018_0794_paper.pdf (2017) Accessed 18 Dec 2022
Liu, Y., Kreimeier, M., Stumpf, E., Zhou, Y., Liu, H.: Overview of recent endeavors on personal aerial vehicles: a focus on the US and Europe led research activities. Prog. Aerosp. Sci. 91, 53–66 (2017). https://doi.org/10.1016/j.paerosci.2017.03.001
Lineberger, R., Hussain, A., Mehra, S., Pankratz, D.M.: Elevating the future of mobility: Passenger drones and flying cars. Deloitte Insights. https://www2.deloitte.com/us/en/insights/focus/future-of-mobility/passenger-drones-flying-cars.html (2018). Accessed 2 May 2023
Zhou, Y., Zhao, H., Liu, Y.: An evaluative review of the VTOL technologies for unmanned and manned aerial vehicles. Comput. Commun. 149(2), 356–369 (2020). https://doi.org/10.1016/j.comcom.2019.10.016
Giligan, M., Grizzle, J.D., Cox, V.H.: Integration of unmanned aircraft systems into the national airspace system: concept of operations v2.0. Federal Aviation Administration (2012)
Kopardekar, P.H.: Unmanned aerial system (UAS) traffic management (UTM): enabling low-altitude airspace and UAS operations. NASA Ames Research Center, Moffett Field, California NASA/TM—2014–218299. https://ntrs.nasa.gov/api/citations/20140013436/downloads/20140013436.pdf (2014) Accessed 13 Jun 2022
European Union Aviation Safety Agency (EASA), Ed.: Concept of Operations for Drones: a risk based approach to regulation of unmanned aircraft. https://www.easa.europa.eu/en/document-library/general-publications/concept-operations-drones (2015). Accessed 2 May 2023
Balakrishnan, K., Polastre, J., Mooberry, J., Golding, R., Sachs, P.: Blueprint For The Sky: The roadmap for the safe integration of autonomous aircraft. https://www.airbusutm.com/uam-resources-airbus-blueprint (2018) Accessed 13 Jun 2022
Geister, G., Korn, B.: Concept for urban airspace integration DLR U-space blueprint: integrating UAS into the future aviation system. Deutsches Zentrum für Luft- und Raumfahrt (DLR). https://www.dlr.de/de/medien/publikationen/sonstige-publikationen/2017/blueprint-concept-for-urban-airspace-integration_2933 (2017). Accessed 2 May 2023
Federal Aviation Administration (FAA), Ed.: Low altitude authorization and notification capability (LAANC) concept of operations. https://www.faa.gov/uas/programs_partnerships/data_exchange/laanc_for_industry/media/laanc_concept_of_operations.pdf (2017) Accessed 13 Jun 2022
Federal Aviation Administration (FAA) and U.S. Department of Transportation (DoT), Eds.: Urban air mobility (UAM): concept of operations v1.0. https://nari.arc.nasa.gov/sites/default/files/attachments/UAM_ConOps_v1.0.pdf (2020) Accessed 15 Nov 2022
Thipphavong, D.P. et al.: Urban air mobility airspace integration concepts and considerations. In: 18th AIAA aviation technology, integration, and operations conference 2018, Atlanta, p. 2018. https://arc.aiaa.org/doi/10.2514/6.2018-3676 (2018) Accessed 16 Nov 2022
European Union Aviaton Safety Agency (EASA), Ed.: U-space regulatory framework workshop: summary of conclusions. https://www.easa.europa.eu/en/newsroom-and-events/events/u-space-workshop-2019 (2019). Accessed 2 May 2023
Sparrow, V., et al.: Aviation noise impacts white paper: state of the science 2019: aviation noise impacts. https://www.icao.int/environmental-protection/Documents/Noise/ (2019)
Vascik, P.D., Hansman, J.R.: Evaluation of key operational constraints affecting on-demand mobility for aviation in the Los Angeles basin: ground infrastructure, air traffic control and noise. In: 17th AIAA Aviation Technology, Integration, and Operations Conference, Denver. https://arc.aiaa.org/doi/10.2514/6.2017-3084 (2017) Accessed 18 Dec 2022
International Transport Forum (ITF) and Organisation for Economic Co-operation and Development (OECD), Eds.: Ready for take-off?: Integrating drones into the transport system. https://www.itf-oecd.org/integrating-drones-transport-system (2021) Accessed 13 Jun 2022
Ackerman, K.A., Gregory, I.M.: Trajectory generation for noise-constrained autonomous flight operations. In: AIAA Scitech 2020 Forum, Orlando. https://arc.aiaa.org/doi/10.2514/6.2020-0978 (2020) Accessed 2 May 2023
Phiesel, D., Moormann, D.: Bewertung des Bodenrisikos beim Betrieb von kleinen unbemannten Fluggeräten: Ein einfacher und quantitativer Ansatz. In: 66. Deutscher Luft- und Raumfahrtkongress: DLRK 2017, München. http://publications.rwth-aachen.de/record/713024 (2017) Accessed 2 May 2023
Duwe, D., Sprenger, A.: Acceptance, preferences and willingness to pay analysis for flying cars and passenger drones. In: 6th International Conference on Innovation in Science and Technology, London. https://www.dpublication.com/abstract-of-6th-ist/6-f220/ (2019) Accessed 18 Dec 2022
Bonk R., et al.: Fit2Fly: a proof of concept for testing the commercial feasibility of unmanned aerial system operations. In: AIAA Scitech 2020 Forum, Orlando. https://arc.aiaa.org/doi/10.2514/6.2020-0740 (2020) Accessed 2 May 2023
Straubinger, A., Rothfeld, R.L., Shamiyeh, M., Büchter, K.-D., Kaiser, J., Plötner, K.O.: An overview of current research and developments in urban air mobility—setting the scene for UAM introduction. JATM 87, 101852 (2020). https://doi.org/10.1016/j.jairtraman.2020.101852
Garrow, L.A., German, B.J., Leonard, C.E.: Urban air mobility: a comprehensive review and comparative analysis with autonomous and electric ground transportation for informing future research. Transportation Research Part C: Emerging Technologies 132(2), 103377 (2021). https://doi.org/10.1016/j.trc.2021.103377
Cohen, A., Guan, J., Beamer, M., Dittoe, R., Mokhtarimousavi, S.: Reimagining the future of transportation with personal flight: preparing and planning for urban air mobility. https://escholarship.org/uc/item/9hs209r2 (2020) Accessed 2 May 2023
Volocopter, Ed.: The Roadmap to scalable Urban Air Mobility: White Paper 2.0. Bruchsal. http://www.volocopter.com/content/uploads/20210324_Volocopter_WhitePaper_Roadmap_to_scalable_UAM_m.pdf (2021) Accessed 3 May 2021
Rajendran, S., Srinivas, S.: Air taxi service for urban mobility: a critical review of recent developments, future challenges, and opportunities. Transportation Research Part E: Logistics and Transportation Review 143(2), 102090 (2020). https://doi.org/10.1016/j.tre.2020.102090
Schweiger, K., Preis, L.: Urban air mobility: systematic review of scientific publications and regulations for vertiport design and operations. Drones 6(7), 179 (2022). https://doi.org/10.3390/drones6070179
Preis, L.: Quick sizing, throughput estimating and layout planning for VTOL aerodromes—a methodology for vertiport design. In: AIAA aviation 2021 forum, virtual event. https://arc.aiaa.org/doi/10.2514/6.2021-2372 (2021)
Allianz pro Schiene: Schiene spart Fläche und lässt Natur ihren Raum: Straße beansprucht über zwölfmal so viel Platz pro berförderter Person, Berlin. https://www.allianz-pro-schiene.de/presse/pressemitteilungen/schiene-spart-flaeche-und-laesst-natur-ihren-raum/ (2021) Accessed 28 Sep 2022
Umwelt Bundesamt: Struktur der Flächennutzung. Dessau-Roßlau. https://www.umweltbundesamt.de/daten/flaeche-boden-land-oekosysteme/flaeche/struktur-der-flaechennutzung (2021) Accessed 28 Sep 2022
Statistisches Bundesamt: Flächennutzung: Bodenfläche insgesamt nach Nutzungsarten in Deutschland. https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Flaechennutzung/Tabellen/bodenflaeche-insgesamt.html;jsessionid=D665CB09025A44A215F0DE6E63EB97FB.live731. Accessed 28 Sep 2022
Statistisches Bundesamt: Personenverkehr: Beförderte Personen in Deutschland. https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Transport-Verkehr/Personenverkehr/Tabellen/befoerderte-personen.html;jsessionid=494BFE665DF01352011B0A0AE3FD716F.live731. Accessed 28 Sep 2022
Preis, L., Hack Vazquez, M.: Vertiport throughput capacity under constraints caused by vehicle design, regulations and operations. In: Delft International Conference on Urban Air-Mobility (DICUAM), Delft/virtual. http://cdn.aanmelderusercontent.nl/i/doc/8fa60b7fcfa71ea900ce2bea2037a151?forcedownload=True (2022) Accessed 5 Apr 2022
Vascik, P.D., Hansman, J.R.: Development of vertiport capacity envelopes and analysis of their sensitivity to topological and operational factors. In: AIAA Scitech 2019 Forum, San Diego. http://arc.aiaa.org/doi/10.2514/6.2019-0526 (2019) Accessed 18 Dec 2022
Zelinski, S.: Operational analysis of vertiport surface topology. In: 39th DASC—Digital Avionics Systems Conference: Virtual conference, October 11–16, 2020: 2020 conference proceedings, San Antonio. http://ieeexplore.ieee.org/document/9256794 (2020) Accessed 18 Dec 2022
Feldhoff, E., Soares Roque, G.: Determining infrastructure requirements for an air taxi service at Cologne Bonn Airport. CEAS, pp. 1–13, (2021) https://doi.org/10.1007/s13272-021-00544-4
Taylor, M., Saldanli, A., Park, A.: Design of a vertiport design tool. In: 2020 Integrated Communications Navigation and Surveillance Conference (ICNS), Herndon. http://ieeexplore.ieee.org/document/9222989 (2020) Accessed 18 Dec 2022
Petersen, J.D., Alexander, R.J., Swaintek, S.S.: Dynamic Vertiport Configuration. US 2020/0226937 A1, USA 16 / 248,170, Jun 16. https://patentimages.storage.googleapis.com/4b/a8/76/10e71cb3d92091/US20200226937A1.pdf (2020) Accessed 18 Dec 2022
International Organization for Standardization (ISO): ISO/AWI 5491: Vertiports—Infrastructure and equipment for Vertical Take-Off and Landing (VTOL) of electrically powered cargo Unmanned Aircraft System (UAS). http://www.iso.org/standard/81313.html. Accessed 1 Jun 2021
Lineberger, R., Hussain, A., Metcalfe, M., Rutgers, V.: Infrastructure barriers to the elevated future of mobility: Are cities ready with the infrastructure needed for urban air transportation? Deloitte Insights. https://www2.deloitte.com/us/en/insights/focus/future-of-mobility/infrastructure-barriers-to-urban-air-mobility-with-VTOL.html (2019)
Lilium: Designing a scalable vertiport: taking the sci-fi vision of landing pads and turning it into a realistic and affordable transit option. https://blog.lilium.com/designing-a-scalable-vertiport-c12e75be1ec5
Johnston, T., Riedel, R., Sahdev, S.: To take off, flying vehicles first need places to land: the buzz about vehicles flying above hides the infrastructure challenge below. https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/to-take-off-flying-vehicles-first-need-places-to-land (2020) Accessed 18 Dec 2022
Hack Vazquez, M.: Vertiport Sizing and Layout Planning through Integer Programming in the Context of Urban Air Mobility. Master thesis, Technical University of Munich (TUM), Munich. https://mediatum.ub.tum.de/node?id=1624149 (2021) Accessed 2 May 2023
Federal Aviation Administration (FAA), Ed.: Engineering Brief No. 105, Vertiport Design. https://www.faa.gov/newsroom/faa-releases-vertiport-design-standards-support-safe-integration-advanced-air-mobility (2022) Accessed 28 Sep 2022
Federal Aviation Administration (FAA): Heliport Design. AC 150/5390–2C. https://www.faa.gov/airports/resources/advisory_circulars/index.cfm/go/document.current/documentnumber/150_5390-2 (2012) Accessed 13 Jun 2022
European Union Aviaton Safety Agency (EASA): Second Publication of Proposed Means of Compliance with the Special Condition VTOL. MOC-2 SC-VTOL. https://www.easa.europa.eu/en/downloads/136697/en (2022) Accessed 28 Sep 2022
Preis, L., Hornung, M.: Vertiport operations modeling, agent-based simulation and parameter value specification. Electronics 11(7), 1071 (2022). https://doi.org/10.3390/electronics11071071
Preis, L., Cheng, S.: Simulation of individual aircraft and passenger behavior and study of impact on vertiport operations. In: AIAA Aviation 2022 Forum, Chicago. https://arc.aiaa.org/doi/abs/10.2514/6.2022-4074 (2022)
Volocopter: VoloCity: Design specifications. Calculated approximations not yet tested in flight. https://www.volocopter.com/solutions/volocity/ (2019) Accessed 22 Jul 2021
AIRBUS: Airbus CityAirbus. https://transportup.com/airbus-cityairbus/. Accessed 22 Dec 2021
Joby Aviation: Joby S4. https://www.jobyaviation.com/ Accessed 21 Dec 2021
AIRBUS: Vahana: Our single-seat eVTOL demonstrator. https://www.airbus.com/en/innovation/zero-emission/urban-air-mobility/vahana. Accessed 21 Dec 2021
Wisk: Discover the Future of Urban Air Mobility. https://wisk.aero/aircraft/. Accessed 14 Sep 2022
eHang: EHang AAV: The Era of Urban Air Mobility is Coming. https://www.ehang.com/ehangaav. Accessed: 21 Dec 2021
UBER Elevate: UberAir Vehicle Requirements and Missions. https://s3.amazonaws.com/uber-static/elevate/Summary+Mission+and+Requirements.pdf (2018) Accessed 5 Oct 2021
BETA Technologies: Aircraft ALIA-250. https://www.beta.team/aircraft/ Accessed 11 Feb 2022
Nathen, P., Bardenhagen, A., Strohmayer, A., Miller, R., Grimshaw, S., Taylor, J.: Architectural performance assessment of an electric vertical take-o and landing (e-VTOL) aircraft based on a ducted vectored thrust concept. https://lilium.com/files/redaktion/refresh_feb2021/investors/Lilium_7-Seater_Paper.pdf (2021) Accessed 21 Dec 2021
Archer: Maker 101: Introducing Maker. https://www.archer.com/maker. Accessed 14 Sep 2022
Bell: Bell Nexus. https://de.bellflight.com/products/bell-nexus. Accessed 14 Sep 2022
Pipistrel: 801 EVTOL. https://www.futureflight.aero/aircraft-program/801-evtol. Accessed 14 Sep 2022
Autoflight: Prosperity I: Next Generation Manned eVTOL. https://www.autoflight.com/en/products/. Accessed 14 Sep 2022
Preis, L., Hornung, M.: A vertiport design heuristic to ensure efficient ground operations for urban air mobility. Appl. Sci. 12(14), 7260 (2022). https://doi.org/10.3390/app12147260
Funding
Open Access funding enabled and organized by Projekt DEAL.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Data availability
All eVTOL-related data is available under the respective URLs.
Code availability
MATLAB code can be made available upon personal request to lukas.preis@tum.de.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
1.1 A.1 Parameter value specification for baseline scenario
In the following section, the aggregation of parameters as used in the baseline scenario (see Sect. 3.1 and Table 1) will be detailed. The parameter values are taken from Ref. [51] and the aggregation was first used in Ref. [52]. All indices of variables correspond to the IDs presented in Ref. [51].
Approach and landing time tA (see Eq. 3) is the sum of process times for entering the airspace tA,1 = 46.3 s, final hover (and touch-down) tA,2 = 22.9 s, stopping the engines tA,3 = 5.0 s, and cool-down after landing tA,4 = 30.0 s. For the taxi-mode “hover”, the engines are assumed to not be shut of wherefore tA is shorter
Take-off and departure time tD (see Eq. 4) is the sum of process times for starting the engines tD,1 = 4.5 s, (take-off and) initial hover tD,2 = 13.5 s, leaving the airspace tD,3 = 28.7 s and cool-down after take-off tD,4 = 30.0 s. For the taxi-mode “hover”, the engines are assumed to be already running wherefore tD is shorter
Boarding time tB,in (see Eq. 5) is the sum of process times for the passenger entering the gate (included walking into the proximity of the vehicle) tB,2 = 19.7 s and boarding the vehicle tB,3 = 13.5 s. De-boarding time tB,out (see Eq. 6) is the sum of process times for the passenger de-boarding the vehicle tB,4 = 65.8 s and leaving the gate (included leaving the proximity of the vehicle) tB,5 = 26.7 s
1.2 A.2 Forming throughput performance indicators
Section 3.2 defines the throughput performance indicators tppax and tpA. In Fig. 5, the process of how the indicators are formed and then aggregated into rules-of-thumb is visualized for the reference vehicle VoloCity. For further reference, the same process will also be illustrated for eHang 216 (Fig.
11) and Joby S4 (Fig.
12) in the following.
1.3 A.3 How-to apply throughput performance indicators
This article introduced two throughput performance indicators to measure operational efficiency on the ground: “hourly passenger throughput per area” tppax and the reciprocal of “area demand per hourly passenger throughput” tpA. In this appendix examples will be given to help apply the indicators as rules-of-thumb for vertiport sizing.tppax can be used to estimate the possible hourly passenger throughput on a given area (from area to throughput). Say the available area A has 3000 m2 (e.g., 100 by 30 m), there is no charging intended and a VoloCity should be operated on it. In this case, Table 2 can serve as look-up table: with a median throughput capacity of tppax,median = 0.0123 PAX/h/m2, the throughput is estimated to be TPpax = tppax x A = 36.9 PAX/h. Rounded down to the next natural 36 passengers will be catered per hour in the presented scenario. In a second step to reduce the risk of error, a range of throughput capacities could be given, for example from tppax,5% = 0.0079 PAX/h/m2 to tppax,95% = 0.0189 PAX/h/m2. Doing the same calculation for each, it can be said that with a 90% confidence (between 5 and 95%), the range of passengers that can be catered per hour is 23–56.tpA can be used to determine the space requirement for a desired throughput capacity (from throughput to area). Say the desired hourly passenger throughput TPpax = 50 PAX/h and a charging time of 30 min is assumed. In this case, Table 5 can serve as look-up table: with a space demand of tpA,Joby = 82.4 m2/PAX/h, the Joby S4 vehicle would require a total area of AJoby = tpA,Joby x TPpax = 4120 m2. In a second step, the required area could be compared to other vehicles; for example, Beta ALIA-250 and eHang 216. With space demands of tpA,Beta = 118 m2/PAX/h and tpA,eHang = 49.7 m2/PAX/h, this would lead to a total area of 5900 m2 for Beta’s vehicle and 2485 m2 for eHang’s vehicle.
1.4 A.4 Dimensions of vertiport airfield elements
Dimensions of pads, gates, and taxi-ways are driving the layout and design of vertiport airfields (see Sect. 4.1). For the purpose of this article, the relevant dimensions are derived from the FAA “Heliport Design Guidelines” [49] (see Fig.
13) and their relationship to the maximum dimensions of the vehicle is visualized below (Fig.
14). In contrast to the main body of the article, the heliport guidelines are also used to visualize the pad dimensions, so that the comparison rests on an established body of guidelines (as explained in Sect. 2, the studies in this article use the pad dimensions given by the newly released FAA vertiport guidelines [48]).
For pads, the side length and area of the three squares (TLOF, FATO, and Pad Safety) is displayed below. For gates, the diameter and area of the two circles (Gate Area and Gate Safety) is displayed with differentiated measures depending on the taxi-mode (hover or ground). For taxi-ways, the width and area of a segment between two gates is displayed, also distinguishing between taxi-modes.
Finding the best gate-to-pad ratio is an important factor in choosing the optimal vertiport layout, as was hinted on in Sect. 2.4. The quotient of pad safety to gate safety is shown in Fig.
15. Different safety standards apply for gates (and taxi-ways) depending on the taxi-mode, which are treated in detail in the previous publication by Preis [32]. As can be seen, there is a trade-off between the modes of taxi at around 9 m of maximum vehicle dimension. Generally speaking, the slopes of both curves are falling, which can be interpreted as gates being more performant according to tppax for small vehicles compared to large vehicles, when being in the trade-off situation with pads. Or, in other words, small vehicle operators have an interest in freeing up pads quickly, because they are more “space-costly” in relative terms. The quotient shown in Fig. 15 is the number of gates that would fit into the same area as one pad.
1.5 A.5 Comparing space demands of transport modes
Throughout this article, the space demands for different transport modes are mentioned, which will be directly compared in this section. There are three sources that will be compared: first, the space demand of existing modes of transport (see Sect. 1); second, area requirements for vertiports as estimated in the scientific literature (see Sect. 2); and third, the area demand of various eVTOL designs as calculated in this article (see Sect. 4.2). A comparative summary of all space demands is shown in Table
6. Before listing the transport modes, the following key assumption needs to be emphasized: the space demand for eVTOLs is purely based on the airfield and does not consider the passenger terminal or other UAM facilities.
To easily compare the transport modes, the space demands will all transformed into the unit square-meters per hourly passenger. First, the numbers for existing modes of transport are given as space demand per daily passenger. Assuming a bi-model distribution with a morning and afternoon peak, Ref. [66] showed that between 12 and 15% (median 13.5%) of the total daily traffic occurs during a typical peak hour. This means that the number of daily passengers is 7.4 times the number of (maximum) hourly passengers. Applying this factor to transform daily into hourly space demand leads to a 7.4 times higher space demand when wanting to cater the same number of passengers in an hour compared to a full day. Concretely, this results in a space demand per hourly passenger of 481 m2 for cars (65 m2 per daily passenger), 237 m2 for rail (32 m2 per daily passenger), and 4588 m2 for airplanes (620 m2 per daily passenger). Next, the area requirement for eVTOLs in the scientific literature will be considered where Ref. [41] states 420 m2 per hourly vehicle throughput. The number of seats is not specified wherefore four seats will be assumed corresponding to the median number of seats in the review of prominent eVTOL designs in Table 4. Applying Eqs. 1 and 2 leads to a space requirement per hourly passenger of 105 m2. Finally, to indicate the range of results from the different eVTOL designs that were studied in this article, the highest and lowest area demands were selected from the results presented in Table 5: City Airbus with 46 m2/PAX/h and Archer Maker with 221 m2/PAX/h. The comparison in Table 6 shows that eVTOLs have the smallest space demand of all transport modes—this insight needs to be interpreted, as mentioned above, in light of the assumption that only the vertiport airfield was considered.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Preis, L. Estimating vertiport passenger throughput capacity for prominent eVTOL designs. CEAS Aeronaut J 14, 353–368 (2023). https://doi.org/10.1007/s13272-023-00650-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13272-023-00650-5