Optimal Vertiport Airspace and Approach Control Strategy for Urban Air Mobility (UAM)
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sustainability Article Optimal Vertiport Airspace and Approach Control Strategy for Urban Air Mobility (UAM) Kyowon Song Department of Future Mobility, Kookmin University, Seoul 02707, Republic of Korea; kyowon@kookmin.ac.kr; Tel.: +82-2910-6696 Abstract: Recently, urban air mobility (UAM), a new transportation system that can expand urban mobility from 2D to 3D, has been in the spotlight all over the world. For successful implementation of UAM, not only eVTOL aircraft development but also various systems such as UAM traffic man- agement are required; however, research on these areas is still insufficient. Based on the BQA model, in this study, we introduce the balanced branch queuing approach (BBQA) model as a new approach control model that can improve operational efficiency by enabling the landing order to be changed more easily. Through simulation, its effectiveness was verified. The proposed BBQA achieved the identical airspace safety as the BQA model, in addition to showing a superior result to the SBA model in on-time performance (OTP). The vertiport airspace blueprint concept and approach control model proposed in this study are expected to play an important role in future studies in the area of air traffic management in UAM. Keywords: urban air mobility (UAM); UATM; vertiport; approach control; VTOL 1. Introduction Rapid urbanization and sustainability challenges are generating huge interest in new transport concepts [1]. In particular, UAM (urban air mobility), a new transportation system that can expand urban mobility from 2D to 3D, is in the spotlight all over the world. In addition, UAM is recognized as a novel means of transportation with several advantages Citation: Song, K. Optimal Vertiport that can alleviate traffic congestion, make streets safer, and reduce air pollution [2]. NASA Airspace and Approach Control defines UAM as a subset of advanced air mobility (AAM) and expects applications in a Strategy for Urban Air Mobility variety of fields, such as emergency patient transport, cargo transport, and passenger trans- (UAM). Sustainability 2023, 15, 437. port [3]. To make UAM a reality, numerous venture companies, aviation manufacturers, https://doi.org/10.3390/su15010437 and automobile companies around the world are eager to develop UAM aircraft, and many models have succeeded in first flights and are entering the commercialization phase [4–9]. Academic Editors: Honghai Zhang, Xinglong Wang and Hao Liu In addition, the governments of various country are preparing for the commercialization of UAM by creating policies and related systems. In 2020, the FAA presented the “Concept Received: 26 October 2022 of Operations v1.0”, which provides an initial foundational perspective supporting the Revised: 14 December 2022 introduction and incorporation of UAM operations into the National Airspace System Accepted: 23 December 2022 (NAS) [10]. In addition, to materialize this concept, NASA formed the AAM (Advanced Published: 27 December 2022 Air Mobility) working group in March 2020 and continues to discuss all matters related to ecosystem construction with many experts [11]. Recently, preparations for UAM infras- tructure construction were undertaken by announcing the “Engineering Brief No. 105, Copyright: © 2022 by the author. Vertiport Design”, which contains guidelines for vertiport design, and related laws are Licensee MDPI, Basel, Switzerland. planned to be enacted by 2024 [12]. Europe is also preparing for the implementation of This article is an open access article UAM by announcing “NPA 2022-06”, which contains relevant certification regulations and distributed under the terms and comprehensive regulations for UAM operation—the first such regulations in the world [13]. conditions of the Creative Commons In industry, discussions continue on the introduction of digitally based ATM (air traffic Attribution (CC BY) license (https:// management) and the necessary systems and procedures for vertiport operation [14,15]. creativecommons.org/licenses/by/ As such, preparations for the realization of UAM are underway in various stages around 4.0/). the world, especially in advanced aviation countries. Sustainability 2023, 15, 437. https://doi.org/10.3390/su15010437 https://www.mdpi.com/journal/sustainability
Sustainability 2023, 15, 437 2 of 21 1.1. Related Work and Previous Research Since 2018, several researchers have been conducting academic research related to UAM. Several conceptual studies have been conducted, including on the integrated op- eration method of airspace for the introduction of UAM and the communication concept for ATC. Thipphavong et al. described NASA’s initial airspace integration concepts for both emergent and early expanded UAM operations [16]. Vascik, P.D. et al. investigated potential operational constraints that could arise during the implementation or scale-up of a UAM system from the perspective of ground infrastructure, ATC, and noise [17,18]. Furthermore, L. E. Alvarez et al. analyzed how vertiport locations influence operational metrics such as passenger capacity, landing site vehicle capacity, and fleet size through a use case focused on New York City [19]. Balac et al. also proposed concepts for the integration of aerial vehicles in urban transportation systems, focusing mainly on three areas: network design, physical simulation, and demand modeling [20]. Rimjha et al. also estimated passenger demand for urban air mobility (UAM) and analyzed the feasibility of operating such a system in Northern California [21]. Lascara et al. suggested four concept components that could enable the routine integration of UAM traffic in existing terminal area airspace [22]. Another similar study was conducted by Weinert, A. et al. [23], who sug- gested an initial model of terminal operations in which an aircraft landing or taking off via a straight trajectory encounters another aircraft and evaluated the detection and avoidance systems. A small number of studies related to the trajectories or the collision avoidance of individual aircraft for the operation of UAM have been performed. Katz et al. presented an encounter model for the development of a UAM collision avoidance system [24] based on the encounter model proposed by Kochenderfer, Mykel J., et al. [25], and Euclides et al. proposed a simulation framework for measuring the safety and effectiveness of trajectory- based UAM operations in urban environments considering the presence of both manned and unmanned eVTOL (electric VTOL) vehicles [26]. Furthermore, several meaningful studies were conducted on computational guidance algorithms for free-flight aircraft en route in a UAM environment. In 2018, Xuxi Yang et al. [27] proposed a computational guidance algorithm with collision avoidance capability, with subsequent studies consider- ing multiaircraft [28] and communication constraints [29]. In their most recent study [30], they proposed a message-passing decentralized computational guidance algorithm with separation assurance capability for multiple cooperative aircraft in UAM. These studies are closely related to the area control problem normally encountered in airways, which is somewhat less relevant to the present work. Existing studies were primarily conducted on area control during the ATC phase, such as on the trajectories of individual aircraft and collision avoidance. Some related research on arrival sequencing and scheduling has been conducted [31–33]. however, there is a lack of access from an ATC perspective, such as approach control, which occurs around the vertiport. In July 2022, an interesting review paper [34] was published covering discussions between various industries and academia regarding the design and operation of vertiports. In the study, it was mentioned that the initial uncertainty about the name of UAM ground infrastructure was overcome, but studies related to vertiports still tend to describe a vision more than providing a realistic and implementable proposal. In our previous research [35], we proposed a concept for the design of vertiport airspace, which is needed for UAM to work in the real world and is generally applicable. Simulation and algorithm development were used to find the best airspace for each strategy. In the same study, we also proposed the idea of a verti- port terminal control area (VTCA), which is where UAM aircraft approach control occurs. The study also showed that the VTCA has multiple holding points that can be used by eVTOL aircraft, as well as a holding circle that connects the holding points. In addition, the study proposed the SBA method, which allows for free movement between holding circles according to landing order, as well as the BQA concept, which allows for movement only according to the queuing of the route by connecting the designated route between the holding points that can move from the time the airspace is designed. The problem of
Sustainability 2023, 15, 437 3 of 21 finding the minimum airspace according to each proposed method was mathematically formulated, and an algorithm was used to find the best way to design the airspace. The genetic algorithm (GA) was used to accomplish scheduling in [36] by applying it to three scheduling schemes for arriving aircraft. By comparing on-time performance (OTP), hovering time (HT), and ground time (GT), the optimal scheduling approach was determined. The optimal scheduling strategy suitable for UAM was chosen as a strategy to improve OTP by reducing the difference between the estimated arrival time and the actual arrival time and by minimizing the ground time that the UAM aircraft spends in the vertiport. In this research, a three-way approach control model was also discussed. In this model, the idea of a holding point for hovering eVTOL aircraft was proposed. During the research, a BQA model was proposed that emphasizes airspace safety by limiting aircraft flight paths before the airspace is even built. The research also proposed two models: the SBA model, which prioritizes the order in which aircraft arrive while allowing for unrestricted movement in the airspace, and the SBAM model, which introduced the idea of a moving circle to the SBA model. In addition, simulation was used to compare the on-time performance (OTP) and loss of separation (LOS) risks of the proposed model. Although the SBA model was the most punctual, with an OTP of 85.9%, this was not statistically different from the BQA model’s OTP of 85.5%, showing that the best approach control model for UAM was the BQA model, in which LOS risk never occurred. Based on the BQA model from the last study, in this study, we introduce the balanced branch queuing approach (BBQA) model as a new approach control model that can improve operational efficiency by enabling the landing order to be changed more easily. Through simulation, its effectiveness was verified. 1.2. Contributions and Outline of the Paper This study contributes to the literature in the following ways. (1) A new algorithm that uses both SBA and BQA approaches was developed to deter- mine the best way to set up the airspace in the BBQA model. (2) It has been confirmed that the new BBQA model can resolve the landing sequence reversal, which was recognized as a vulnerability of the BQA model in the previ- ous study. (3) A simulation was performed to show that the newly proposed BBQA model is better than the BQA model. The results show that OTP outcomes were improved, whereas airspace safety was unchanged relative to the BQA model. This study is organized as follows. In Section 2, the new model concept and optimal airspace are described. In Section 3, BBQA approach control models are proposed and empirical results obtained through simulation are described, and a discussion of the results and future work are covered in Section 4, followed by the conclusion. 2. New Model: Balanced Branch Queuing Approach Model In previous studies [35], we proposed a branch queuing approach (BQA) and a sequence-based approach (SBA), which are UAM approach control concepts. In the SBA model, vehicles with the fastest arrival sequence in the upper holding circle are moved in a straight line by searching for an empty holding point in the lower holding circle without a fixed path. On the other hand, BQA allows for movement only within a predetermined path between the holding points. In our previous study [36], the BQA model was found to be the most suitable model for the approach control of a multicopter eVTOL aircraft, such as the EHang 184, EHang 216, or Volocopter 2x, which can accommodate one or two passengers. However, this result prioritized navigation safety through urban areas and did not show the best results on the actual OTP side compared to other models. This results from the structural problem of the BQA model, which is caused by landing sequence reversal and the bottleneck phenomenon. In this study, we propose the balanced branch
Sustainability 2023, 15, x FOR PEER REVIEW 4 of 21 did not show the best results on the actual OTP side compared to other models. This re- Sustainability 2023, 15, 437 4 of 21 sults from the structural problem of the BQA model, which is caused by landing sequence reversal and the bottleneck phenomenon. In this study, we propose the balanced branch queuing approach (BBQA) model, which provides identical airspace safety, as well as the queuing approach (BBQA) model, which provides identical airspace safety, as well as the OTP performance of an SBA model. OTP performance of an SBA model. The proposed BBQA model is based on the BQA model and is a concept that allows The proposed BBQA model is based on the BQA model and is a concept that allows for freer movementbetween for freer movement betweenholding holding points points to to improve improve thethe OTP. OTP. As shown As shown in Figure in Figure 1, 1, whereas the BQA model strictly regulates branches, which connect the holding points, the whereas the BQA model strictly regulates branches, which connect the holding points, BBQA the BBQA is aismore a more flexible flexiblemodel modelthat thatallows allows for for movement movement when whenthere thereisisnonopossibility possibility of of collision between eVTOL aircraft. The airspace design concept of the BBQAmodel collision between eVTOL aircraft. The airspace design concept of the BBQA modelmini- mizes the bottleneck phenomenon encountered in the BQA model, minimizes the bottleneck phenomenon encountered in the BQA model, which is caused by which is caused by the occupation the occupation of of thethe holding holdingpoint pointofofthe thepreceding precedingaircraft aircraft in in the the same queue. queue. Therefore, Therefore, the the proposed proposed model model is is expectedtotominimize expected minimizethe thelanding landing sequence sequence reversal reversalphenomenon. phenomenon. Figure1.1.The Figure Theconcepts conceptsofof thethe balanced branch balanced queuing branch approach. queuing approach. In this study, we propose an algorithm that searches for the optimal airspace design In this study, we propose an algorithm that searches for the optimal airspace design for the newly introduced BBQA model, achieving improvements compared to the original for the models as newly introduced verified BBQAexperiments. by simulation model, achieving improvements compared to the original models as verified by simulation experiments. 2.1. BBQA Airspace Design Concept 2.1. BBQA To deduceAirspace Design Concept the optimal airspace design for the BQA model, we suggested a new algorithm that combines To deduce the optimal the methods of the SBA airspace design andBQA for the BQA. The existing model, BQA model we suggested a new al- had a restriction that limited the branches that connect the holding gorithm that combines the methods of the SBA and BQA. The existing BQA model had a points to only integer multiples. restrictionHowever, that limiteddue the to this restriction, branches thateven when connect theit holding had a lower capacity, points to onlytheinteger problem multi- of additional required airspace occurred, as shown in the previous ples. However, due to this restriction, even when it had a lower capacity, the problem of research [36]. The BBQA’s optimal airspace searching process combined the SBA process and BQA process, additional required airspace occurred, as shown in the previous research [36]. The BBQA’s as shown in Figure 2. In the BBQA model, the branch connection between holding points optimal airspace searching process combined the SBA process and BQA process, as shown is not required to be integer multiples nor to have the identical branch queuing shape. in Figure 2. In the BBQA model, the branch connection between holding points is not re- Therefore, based on the airspace design of the SBA’s holding point arrangement, a new quired to was algorithm be integer developed multiples that cannor to have connect thethe identical branches likebranch the BQA.queuing shape. Therefore, based on the airspace design of the SBA’s holding point The first step of designing the airspace for BBQA is identical to the SBA. arrangement, a new First,algorithm find wasradius the developed that can circle of the holding connectandthethebranches like the point optimal holding BQA.arrangement based on the The first algorithm step of suggested in designing the previous thestudy airspace [35]. for TheBBQA secondisstep identical to the the is to arrange SBA. First, find optimal the radius of the holding circle and the optimal holding point branch in the airspace determined by methods identical to the SBA. The key to arranging arrangement based on the algorithm the optimal suggested branch is toin thethe find previous optimalstudy [35]. The pair between thesecond holdingstep is to points arrange that thetwo are in the optimal adjacent branch in holding circles, determined the airspace as shown in by Figure 3. In other methods words, identical to the adjacent SBA. Theholding key to point arranging should be ablebranch the optimal to be connected is to findwithout causing the optimal anybetween pair collision the for all pairs that holding can be points thatmoved. are in the As shown in Figure 3, if there two adjacent holding circles, as shown are n 1 holding points in the inner holding in Figure 3. In other words, the adjacent circle and there holding are n2 holding point should points be ableintothe beouter holding connected circle, itcausing without can be altered to a problem any collision for alltopairs find that the can optimal be moved.linkAsthatshown connects n1 + n23,nodes. in Figure If the if there are shortest holding n1 + points n2 pathsinout theof n1 ×holding inner n2 pathscircle are selected, the optimal combination can be found where no collision between branches and there are holding points in the outer holding circle, it can be altered to a problem occurs, even when the adjacent holding points are connected. Therefore, in this research,
Sustainability 2023, 15, x FOR PEER REVIEW 5 of 21 Sustainability 2023, 15, x FOR PEER REVIEW 5 of 21 to find the optimal link that connects + nodes. If the shortest + paths out of Sustainability 2023, 15, 437 5 of 21 to× find paths are selected, the optimal link thatthe optimal connects +combination nodes. If can be found the shortest where no collision + paths out of be- tween × branches paths areoccurs, even selected, thewhen thecombination optimal adjacent holding can be points are connected. found where Therefore, no collision be- in we this tweenresearch, branches performed we performed occurs, steps even when to produce steps to produce the adjacent a distance a distance matrixholding betweenpoints matrix nodesare between connected. and nodes Therefore, to find the shortest and to find the nin × n shortest this research, number of ×performed innumber wepaths of to steps the distance paths in the produce matrix. distancematrix a distance matrix. between nodes and to 1 2 find the shortest × number of paths in the distance matrix. Figure 2. BBQA airspace design concept : Hybrid of SBA and BQA. Figure 2.2.BBQA Figure BBQAairspace airspace design conceptHybrid design concept: : Hybrid of SBA of SBA andand BQA. BQA. Figure 3. Branch design of BBQA. Figure 3. Branch design of BBQA. 2.2. Algorithm 2.2. Figure Algorithm for 3. Branch for the BBQA the design BBQA Airspace Airspace of BBQA. The algorithm The algorithm to to find find BBQA BBQA model’s model’s optimal optimal airspace airspace was was upgraded upgraded basedbased on on the the algorithm algorithm 2.2. Algorithm sinfor sin thethe the previous previous study BBQAstudy [35]. Identical [35]. Airspace Identicalto to SBA SBA and and BQA, BQA, as as itit was was more more important important to to find find an an exact exact design design than than having having aa faster faster solving solving speed, speed, more more focus focus was was put put on on finding finding the The algorithm to find BBQA model’s optimal airspace was upgraded based 1on the the exact exact solution solution through through aa full full search search rather rather than than using using heuristic heuristic methods. methods. Algorithm Algorithm 1 algorithm shows shows the sin the the previous algorithm’s algorithm’s study [35]. pseudocode pseudocode to Identical to search search for to theSBA for the andairspace optimal optimal BQA, asof airspace itBBQA. of was more BBQA. important Through Through to the findgiven the an exact given designthe algorithm, algorithm, than the having most most optimal optimal a faster holding holdingsolving point speed, more holding point arrangement, arrangement, focus was putradius, circle on finding radius, theandexact solution branch design through for BBQA a full can and branch design for BBQA can be determined.search be rather determined. than using heuristic methods. Algorithm 1 shows the algorithm’s pseudocode to search for the optimal airspace of BBQA. Through theAlgorithm given algorithm, the most 1. Pseudocode optimal holding for determining pointairspace the optimal arrangement, holding algorithm circle radius, for BBQA and branch design for BBQA can be determined. Given: : Max holding time : Min1. Algorithm time separation for Pseudocode between takeoff and determining thelanding optimal airspace algorithm for BBQA Given: : Minimum separation distance between eVTOL aircrafts. Initialize: : Max holding time : Min time separation between takeoff and landing : Minimum separation distance between eVTOL aircrafts. Initialize:
Sustainability 2023, 15, 437 6 of 21 Algorithm 1. Pseudocode for determining the optimal airspace algorithm for BBQA Given: th : Max holding time ∆S : Min time separation between takeoff and landing du : Minimum separation distance between eVTOL aircrafts. Initialize: K = 1 // Initial number of holding circles ropt = 500 // Initial optimal radius of the outermost holding circle Ct = 2×th∆S // Calculate maximum airspace capacity of VTCA. Repeat Find the combination of ni satisfying constraint ∑ ni = Ct FOR All combinations of ni du ri = MAX ◦ , ri−1 + du // Calculate the radius of each holding circle i. 2×sin 180 n i rout = rk // Radius of the outermost holding circle END FOR IF ropt ≥ MI N [rout ] THEN ropt = MI N [rout ] k = k + 1 // Increase the number of holding circles. ELSE Stop. No improved solution found via the swap. END IFUNTIL Stop FOR all holding circles FOR x = 1 to ni of holding circle i FOR y = 1 to ni+1 of holding circle i + 1 DIST(x, y) = distance between two points // Calculate distance matrix END FOR END FOR Find the ni + ni+1 shortest distance // find optimal branch design END FOR The BBQA model has basically an identical process of finding the optimal airspace to that of SBA and can produce various airspace designs including the radius of the outermost holding circle, like SBA. The problem of finding the optimal airspace can be expressed by a simple mixed-integer programming (MIP) formulation: Min router du s.t ri ≥ 180◦ 2×sin ni r i ≥ r i −1 + d u ∑ ni = Ct 0 ≤ ni ≤ Ct , ni is integer (ni ∈ Z) where ri : the radius of each holding circle (i); ni : the number of holding points on holding circle i; du : minimum separation distance between eVTOL aircraft; Ct : maximum airspace capacity of VTCA. The objective function seeks to minimize the radius of the outermost holding circle in the vertiport terminal control area (VTCA), which suggests using a minimal airspace to operate the vertiport. If the radius is the same, the BBQA model was designed to select the optimal airspace design using D HP , which was applied in SBA as well. D HP is an indicator that checks the difference between the maximum number of holding points in the holding circle and the actual number of holding points, and if D HP is smaller, the airspace design has adequate distribution of holding points. Figure 4 shows an instance in which there are five holding circles, with a maximum airspace capacity of VTCA of 60 eVTOL aircraft. Using the identical airspace as this example, it can be seen that the last instance in which the holding point has D HP = 72.3 and is well-distributed is the optimal airspace design.
Sustainability Sustainability2023, 2023,15, 15,x 437 FOR PEER REVIEW 7 of 2121 7 of Variousairspace Figure4.4.Various Figure airspacedesigns designsofofBBQA BBQAwith withthe thesame sameradius. radius. 2.3. Simulation Results 2.3. Simulation Results We conducted a simulation to determine the optimal airspace for the BBQA strategy. We 5conducted Figure shows thea simulation simulation resultto determine for each the optimal model andairspace the optimalfor the BBQA model airspace strategy. fol- Figure 5 shows the simulation result for each model and the optimal lowing the number of holding circles. The radius of the outermost holding circle decreased airspace model follow- ing the the when numberholdingof holding circles. The circle increased fromradius one tooffour the for outermost SBA, BQA, holding circle decreased and BBQA, but when when the holding circle increased from one to four for SBA, there were five holding circles, the radius increased, producing an inefficient airspaceBQA, and BBQA, but when there were five holding circles, the radius increased, producing design. In addition, the BQA needed more airspace than SBA or BBQA, as it has restrictions an inefficient airspace de- sign. In addition, the BQA needed more airspace than SBA or on its branches. The newly introduced BBQA requires identical airspace as SBA but also BBQA, as it has restrictions on its branches. finds the optimal The newlyshape branch introduced between BBQA holding requires points. identical airspacethe BQA increased as airspace SBA but safety also finds the optimal branch shape between holding points. BQA by restricting the paths of eVTOL aircraft with branches with the airspace design but increased the airspace safety byrequired restricting the paths of significantly eVTOL larger aircraft airspace with than SBA branches and proved with the to beairspace design inefficient but re-of in terms quired space significantly efficiency. As larger shown airspace in Figurethan5,SBA and proved if there are twoto be inefficient holding circles,inBQA terms of space requires an efficiency. As shown airspace radius in Figure of 115.7 5, if there m, whereas SBAare two holding requires circles, an airspace BQAofrequires radius 106.1 m.anInairspace all cases, radius of 115.7 with three m, whereas to five SBA requires holding circles, it can beanseen airspace radius that the of 106.1 required m. In of airspace allBQA cases, is with larger three to five holding circles, it can be seen that the required than that of SBA. BBQA used the same airspace as SBA and achieved the same airspace airspace of BQA is larger than that of SBA. safety as BQA.BBQA used the same airspace as SBA and achieved the same airspace safety as BQA. To check whether the suggested algorithm can find the optimal airspace effectively To check under various whether the suggested conditions, the parameters algorithm thatcan findthe affect theairspace optimal design airspacewere effectively altered. under various conditions, The simulation the parameters was performed with the that maxaffect holdingthe time airspace (th ) design assumed were altered. to be 1200 s, Thethe simulation minimumwas time performed separation with the max between holding takeoff andtime ( ) assumed landing (∆S ) assumed to be 1200 to bes,5~15the mini- s, and the minimum mum time separationseparation between distance takeoffbetween and landing eVTOL ( )aircraft assumed (duto ) in bethe 5~15 range s, and ofthe20~40min-m. The simulation imum separation result distanceis shown betweenineVTOL Figure aircraft 6. Through ( ) inthis theresult, rangeitofcan be seen 20~40 m. The that even simu- with result lation various changes is shown inin conditions, Figure the optimal 6. Through airspace this result, it cansearch be seenfor BBQA that evenperforms with various well. In addition, changes it can bethe in conditions, seen that the optimal shapesearch airspace of theforairspace is determined BBQA performs well. In ∆S . When byaddition, it ∆S be can is seen constant, the shape that the airspaceof thechanges airspacein size dependingby is determined on the du , but . When the branch shape is constant, the and holding airspace changes point arrangement in size dependingremain , but the on the identical. ∆Sbranch determines shapethe andmaximum holding point airspace ar- rangement remain identical. determines the maximum airspace capacity of VTCA and therefore affects the airspace shape, but d only affects u capacity of VTCA the sizeandand affects the airspace shape, but only affects the size and not the shape. not the shape. therefore
Sustainability2023, Sustainability 15,x437 2023,15, FOR PEER REVIEW 88 of of2121 Figure 5. Airspace design for each model under the same conditions. (1), (2),... (5) means the number Figure 5. Airspace design for each model under the same conditions. (1), (2),... (5) means the num- of holding ber circles. of holding circles.
Sustainability 2023, 15, 437 9 of 21 Sustainability 2023, 15, x FOR PEER REVIEW 9 of 21 Figure 6. Various airspace designs of BBQA with parameter changes. Figure 6. Various airspace designs of BBQA with parameter changes.
Sustainability 2023, 15, x FOR PEER REVIEW 10 of 21 Sustainability 2023, 15, 437 10 of 21 3. Approach Control Using BBQA Model 3. Approach Control Using BBQA Model 3.1. 3.1. Development Development of of BBQA BBQA Model Model The The BBQA model operates similarly BBQA model operates similarly toto the the BQA BQA modelmodel proposed proposed in in our our previous previous research research [36]. However, in the airspace design stage, it is not restricted in its branch count, [36]. However, in the airspace design stage, it is not restricted in its branch count, with increased in with increased inspace spaceefficiency efficiencybybyusing using SBA’s SBA’s airspace. airspace. Figure Figure 7 shows 7 shows the the approxi- approximate mate framework framework of the of the BBQA BBQA model.model. The BBQAThe BBQA model consists model consists of twosimilar of two stages stages to similar to existing existing models. The first step is a preparation step for the implementation models. The first step is a preparation step for the implementation of the model. In the of the model. In the first firstthe step, step, the parameters parameters necessary necessary for airspace for airspace design and design and approach approach control arecontrol are inserted, inserted, and based on this input, the optimal airspace, which includes and based on this input, the optimal airspace, which includes the radius of the holding the radius of the holding circle, thecircle, the holding holding point arrangement, point arrangement, and count, and branch branchiscount, is constructed. constructed. In addi- In addition, the tion, basicthe basic initialization, initialization, whereby whereby the flight the dataflight data are are loaded and loaded and the the initial ETAinitial ETA (esti- (estimated time mated timeisof of arrival) arrival) isiscalculated, calculated, performed. is performed. Figure 7. The framework of the BBQA model. Figure 7. The framework of the BBQA model. During the second secondstep, step,real realapproach approachcontrol controlisis performed. performed. InInthethe flight flight status status data, data, all all information information required required for approach for approach control, control, such as such the as the coordinates coordinates of the vehicles, of the vehicles, landing landing sequence, sequence, and vehicle andoccupancy vehicle occupancy of holding of points, holdingispoints, included is included and checked and and checked updatedand updated in real time. in real time. Ifatlanding If landing a vertiport at aisvertiport possible, is possible, then a searchthen a search isfor is performed performed vehicles that for vehicles that can land in holding circle 1. If there is such a vehicle, can land in holding circle 1. If there is such a vehicle, then it gives landing clearance to then it gives landing clearance the aircrafttowith the the aircraft with highest the highest priority in the priority in the landing landing sequence. At thesequence. same time, Atitthe same searches time, it searches for a vehicle thatfor a vehicle can enter the that can enter empty holdingthe point, empty which holding point, which becomes becomes unoccupied as un- the aircraft that occupied as are the granted aircraft landing that are clearance in holding granted landing circle 1inbegin clearance the landing holding sequence. circle 1 begin the Unlike BQA, landing sequence.because BBQA Unlike is connected BQA, with diverse because BBQA branches, is connected witha diverse vehicle branches, with the fastest a ve- sequence is chosen among the eligible vehicles. This process is hicle with the fastest sequence is chosen among the eligible vehicles. This process is iden- identical all the way to holding circle N. The vehicles that are granted landing or moving tical all the way to holding circle N. The vehicles that are granted landing or moving clear-clearance move through a predetermined ance move through route. Additionally, they a predetermined update route. the landingthey Additionally, sequence update of the the flight landingstatus se- data in real quence of the time by status flight performing data in GA-based real timeoptimal scheduling. by performing GA-based optimal scheduling. The biggest improvement improvementof ofthe theBBQA BBQAmodel modelcompared comparedtoto the theexisting existing BQA BQAis the re- is the duction of the bottleneck phenomenon by designing a more flexible reduction of the bottleneck phenomenon by designing a more flexible branch structure. If branch structure. If the bottleneck the bottleneck phenomenon phenomenon is decreased, the occurrence is decreased, the occurrence of landing reversal of landing is also is reversal decreased, also de- and the landing sequence calculated through schedule optimization creased, and the landing sequence calculated through schedule optimization can main- can maintained. tained.Figure 8 is a sample of an approach control with the BBQA model applied. The parts marked in shades of red represent vehicles with the moving clearance traveling. If the branch structure were the simple BQA, then the eVTOL entering holding circle 2 would be
Sustainability 2023, 15, x FOR PEER REVIEW 11 of 21 Figure 8 is a sample of an approach control with the BBQA model applied. The parts Sustainability 2023, 15, 437 11 of 21 marked in shades of red represent vehicles with the moving clearance traveling. If the branch structure were the simple BQA, then the eVTOL entering holding circle 2 would be 26, not 36. However, the BBQA grants moving clearance to eVTOL number 26, which 26, not 36. However, the BBQA grants moving clearance to eVTOL number 26, which has a hasfaster a faster landing landing sequence. sequence. Through Through this procedure, this procedure, the landing the landing sequencesequence control effi- control efficiency ciency can be maximized. can be maximized. Figure Figure A sample 8. sample 8. A ofofBBQA BBQAmodel model application. application. 3.2. Advantages of the BBQA Model 3.2. Advantages of the BBQA Model To check the quality of the suggested BBQA model, a series of simulation experiments To check were the quality performed. of theit suggested To compare BBQA BQA, with the existing model, a series SBA, and SBAMof simulation models, we experi- performed ments a total of 200 were performed. Tosimulations: compare it with100 different flightBQA, the existing schedules SBA,andandscenarios using we SBAM models, 2 differenta scheduling performed total of 200 strategies. We analyzed simulations: the 12,000 100 different vehicle flight flight data schedules andpoints deduced scenarios using 2 from this scenario. In addition, we compared the result to the existing different scheduling strategies. We analyzed the 12,000 vehicle flight data points deducedmodels and checked the performance of the BBQA. from this scenario. In addition, we compared the result to the existing models and checked Figure 9 is a histogram of BQA and BBQA’s delay times. To check the excellence theofperformance of the BBQA. the suggested BBQA model, a series of simulation experiments were performed. The Figure 9 is a histogramaccording delay time was calculated of BQA and to the BBQA’s differencedelay times.the between Toactual checktime theofexcellence arrival of the(ATA) suggested and the BBQA model, scheduled a series time of simulation of arrival (STA); if thisexperiments value is negative,wereit performed. The de- signals an early layarrival. time was Thecalculated delay time according distributiontodifference the difference between between BQA and theBBQA actualwas time not of arrival very (ATA) and theHowever, significant. scheduled the time BBQAofmodel, arrival (STA);to similarly if existing this value is negative, models, had delayit signals an early times closer to zero arrival. Thewhen delay using timethe genetic-algorithm-based distribution difference betweenscheduling BQAmodeland BBQAcompared was tonotwhen very sig- nificant. However, the BBQA model, similarly to existing models, had delay that using the first-come-first-served (FCFS) model. Accordingly, it can be confirmed timesthecloser scheduling strategy that minimizes the deviation of STA and ATA is implemented well in to zero when using the genetic-algorithm-based scheduling model compared to when us- the BBQA model. ing the first-come-first-served (FCFS) model. Accordingly, it can be confirmed that the As another indicator that can check the excellence of BBQA, the punctuality was scheduling calculatedstrategy that In as follows. minimizes this study,theit deviation was assumed of STA thatand ATAwithin arriving is implemented 2 min of thewell in theplanned BBQA model. schedule was on time. As another indicator that can check the excellence of BBQA, the punctuality was cal- culated as follows. In this study, it was assumed that arriving within 2 min of the planned schedule was on time. ∑ On-Time Performance (OTP) = ×100, 1, if ≤ + 2 where = ; 0, otherwise : the number of UAM eVTOL aircraft.
Sustainability 2023, 15, Sustainability 2023, 15, 437 x FOR PEER REVIEW 1212of of 21 21 Figure 9. Delay time for BQA and BBQA models. Figure 9. Delay time for BQA and BBQA models. N Punctuality is an immensely important element not ∑ only V xi in commercial airlines but On − Time Performance (OTP) = i=1 × 100, also in UAM. UAM has to operate a vertiport inside a limiting NV urban space, and if oper- ated as air taxis, the link between 1, if ATAi ≤ STAi + 2 other forms of transportation is important; therefore, where x the arrival i = punctuality ; has great significance. Therefore, in UAM approach control, OTP 0, otherwise (on-time performance) Nv : the number of UAM is an index eVTOLthat can prove the excellence of models. Figure 10 aircraft. shows the OTP result for each model Punctuality is an immensely important with a comparison element not of OTP only in for FCFS andairlines commercial GA-based but scheduling. As in the results shown in Figure 10, the OTP of all models also in UAM. UAM has to operate a vertiport inside a limiting urban space, and if operated improved signifi- cantly whenthe as air taxis, GA-based scheduling link between otherwasformsapplied. Additionally, of transportation the BBQA model is important; had the therefore, an OTP of punctuality arrival 86.3%, which hasisgreat an even better rateTherefore, significance. than thatin ofUAM the SBA model.control, approach The newly OTPsug- (on- gested BBQA achieved time performance) is anidentical index that airspace safety can prove theas BQA, in addition excellence of models. to Figure showing10 ashows superior the result to that OTP result forofeach the SBAmodel in terms with aof OTP. In other comparison words, of OTP it proved for FCFS andto be the model GA-based which scheduling. was As inthe thesafest and results the most shown efficient in Figure 10,in terms the OTPof ofapproach all models control. improved significantly when GA-based scheduling was applied. Additionally, the BBQA model had an OTP of 86.3%, which is an even better rate than that of the SBA model. The newly suggested BBQA achieved identical airspace safety as BQA, in addition to showing a superior result to that of the SBA in terms of OTP. In other words, it proved to be the model which was the safest and the most efficient in terms of approach control.
Sustainability Sustainability 2023, 2023, 15, 15, x437 FOR PEER REVIEW 1313ofof21 21 Figure 10. Figure On-timeperformance 10. On-time performance results results for for each each model. model. AAsimilar similarresult resultcan canbebe found found in the in the landing landing sequence sequence reversal reversal occurrence occurrence status. status. The The BQA and BBQA models restrict vehicle movement strictly to branches and show a BQA and BBQA models restrict vehicle movement strictly to branches and show a bottle- bottleneck phenomenon due to queuing in identical branches. The bottleneck phenomenon neck phenomenon due to queuing in identical branches. The bottleneck phenomenon causes a reversal effect, whereby the aircraft is granted landing clearance, although its causes a reversal effect, whereby the aircraft is granted landing clearance, although its sequence is later, in contrast to the actual aircraft landing sequence. Table 1 shows the sequence is later, in contrast to the actual aircraft landing sequence. Table 1 shows the result that compares the landing sequence reversal of BQA and BBQA. Here, the BBQA result that compares the landing sequence reversal of BQA and BBQA. Here, the BBQA model was tested in two different situations. BBQA_A applied the newly suggested BBQA model was tested in two different situations. BBQA_A applied the newly suggested BBQA model perfectly. In other words, it searched for a more efficient airspace than in the airspace model perfectly. In other words, it searched for a more efficient airspace than in the air- design step. Therefore, the simulation began in a smaller airspace than the BQA model, space design step. Therefore, the simulation began in a smaller airspace than the BQA as in Table 1 and Figure 11. The holding point arrangement showed a different shape to model, as in Table 1 and Figure 11. The holding point arrangement showed a different that of BQA as well. However, BBQA_B used BQA’s holding point arrangement and radius shape to that of BQA as well. However, BBQA_B used BQA’s holding point arrangement of the holding circle and used BBQA’s branch design. This was compared for both cases and radius because it isofnot thepossible holdingtocircle and how compare usedapproach BBQA’s branch controldesign. This was purely effects compared landing for sequence both cases because it is not possible to compare how approach control reversal if the airspace size and holding point arrangements are different. purely effects land- ing sequence reversal if the airspace size and holding point arrangements are different. Table 1. Holding circles for comparison of landing sequence reversal. Table 1. Holding circles for comparison of landing sequence reversal. Radius (m) # of Holding Points Radius (m) # of Holding Points Holding Circle 1 2 3 4 1 2 3 4 Holding Circle 1 2 3 4 1 2 3 4 BQA BQA 32.4 32.4 52.4 52.4 72.4 72.4 92.4 92.4 1010 1010 2020 20 20 BBQA_A 17 38.6 58.6 79.8 5 12 18 25 BBQA_A 17 38.6 58.6 79.8 5 12 18 25 BBQA_B 32.4 52.4 72.4 92.4 10 10 20 20 BBQA_B 32.4 52.4 72.4 92.4 10 10 20 20
Sustainability 2023,15, Sustainability2023, 15,437 x FOR PEER REVIEW 14 of 21 14 of 21 Figure 11.Airspace Figure11. Airspacedesign designfor forcomparison comparisonofoflanding landingsequence sequencereversal. reversal. As Asreflected reflectedby bythe theresults resultspresented presentedininTable Table2,2,there therewas wasan anaverage averageof of3.8 3.8landing landing sequence sequence reversal and 3 instances which showed 8 reversal phenomena in the BQA model. reversal and 3 instances which showed 8 reversal phenomena in the BQA model. InIncomparison, comparison,thetheBBQA_A BBQA_Ashowed showedan anaverage averageof of5.8 5.8reversal reversalphenomena, phenomena,whichwhichisismore more than thanthe theBQA BQAmodel. model.However, However, asas shown shownin Figure 11, 11, in Figure it isitunreasonable to directly is unreasonable compare to directly com- itpare withitBQA, as the as with BQA, result was derived the result from an was derived airspace from different an airspace than that different thaninthat the in BQAthe mode. It is also shown in Figure 11 that although there were more reversal phenomena, the BQA mode. It is also shown in Figure 11 that although there were more reversal phenom- OTP improved significantly. The result of BBQA_B, which was conducted under identical ena, the OTP improved significantly. The result of BBQA_B, which was conducted under airspace conditions as BQA, showed a definite decrease in reversal phenomena. There was identical airspace conditions as BQA, showed a definite decrease in reversal phenomena. an average of 2.76 reversals and even 4 cases in which no reversal occurred. In an airspace There was an average of 2.76 reversals and even 4 cases in which no reversal occurred. In identical to BQA, the BBQA model showed an improvement in landing sequence reversal. an airspace identical to BQA, the BBQA model showed an improvement in landing se- quence reversal. Table 2. Comparison results of landing sequence reversal. Table 2. Comparison Landing Sequenceresults of landing sequence reversal. BQA BBQA_A BBQA_B Reversal Landing Sequence BQA BBQA_A BBQA_B 0 Reversal - - 4 10 8- -- 164 21 178 -- 2816 3 24 4 21 2 17 - 28 4 17 12 16 53 24 17 254 1321 64 17 10 12 23 216 75 17 4 25 29 13 86 3 10 6 23 2 97 -4 1 29 8 Total 1003 1006 100 9 - 1 Total In this study, we also compared 100 airspace safety using 100 the LOS (loss of100 separation) concept. LOS indicates that the distance between two aircraft is not secured by the minimum safetyIndistance this study, [37].we In also compared addition, airspaceerror operational safety using (OE) the LOS is when LOS(loss of separation) occurs due to an concept. LOS indicates that the distance between two aircraft is not air traffic control mistake, as defined by the FAA’s Air Traffic Organization (ATO) secured by theandmini- is mum safety distance [37]. In addition, operational error (OE) is when LOS classified into four hazardous stages. Table 3 shows the occurrence status and occurrence occurs due to an air traffic control mistake, as defined by the FAA’s Air Traffic Organization ratio of the OE’s danger stage for each model. Identical to the BQA model, the BBQA model (ATO) and is classified only enabled into four travel hazardous on routes where stages. Tabledistance the safety 3 showswas thesecured occurrence status due to and occur- branches in its rence ratio airspace of the design OE’s and dangerdid therefore stage notfor each model. generate OE inIdentical both the to the BQA FCFS and GAmodel, the BBQA methods. It is model only enabled travel on therefore confirmed to be a safe model. routes where the safety distance was secured due to branches in its airspace design and therefore did not generate OE in both the FCFS and GA methods. It is therefore confirmed to be a safe model.
Sustainability 2023, 15, 437 15 of 21 Table 3. Comparison of airspace safety for each model. BQA /BBQA SBA SBAM OE Severity FCFS GA FCFS GA FCFS GA Proximity Events 3139 3152 69 64 - - (PE) (1.77%) (1.78%) (0.04%) (0.04%) Low Risk 2234 2093 378 369 - - (LR) (1.26%) (1.18%) (0.21%) (0.21%) Moderate Risk 1151 1142 - - - - (MR) (0.65%) (0.65%) High Risk 983 835 - - - - (HR) (0.56%) (0.47%) 5373 5245 2581 2410 OE Total - - (3.04%) (2.96%) (1.46%) (1.36%) The BBQA model, which was developed to strengthen BQA model’s weaknesses, showed equal airspace safety as BQA and that same space-usage efficiency as SBA, as well as an improvement in OTP compared to SBA from the previous test results. Furthermore, by fixing BQA’s weakness, i.e., the landing sequence kept breaching due to branches, the landing sequence reversal was improved. 3.3. Simulation and Empirical Results In this study, in order to determine how the BBQA model performs in different sce- narios, we conducted simulations on BBQA and analyzed the results. The basic parameter settings were set as shown in Table 4, and we conducted the simulation in various situations by adjusting the data generation standard OTP and average landing interval (ALI). Table 4. BBQA simulation parameters. Parameter Value STAi (scheduled time of arrival) U (1, 20 ) th (max holding time) 1200 s ∆S (min time separation between takeoff and landing) 10 s Ct (max airspace capacity of VTCA) 60 vehicles Sv (vertical speed of approach) 2.5 m/s Sc (vertical speed of approach) 3 m/s du (min separation distance between eVTOL aircrafts) 20 m Figure 12 shows the overall performance scenarios of the simulation. The flight data generation needed for the simulation were OTP 70%, 75%, 80%, 85%, and 90%, performed for five different cases and produced 100 sets of flight data per case. The standard for generating flight data was set to 60 eVTOL aircraft attempting to approach for 20 min, so an increase in OTP indicates a possible eVTOL aircraft overload in a certain time period. In addition, for each case, the average landing interval (ALI) was set as 20 s, 25 s, and 30 s. ALI is a parameter that can reflect the vertiport’s landing capacity and can guarantee the ∆S of the simulation, in addition to reflecting the congestion of the vertiport. The simulation was performed on both BQA and BBQA under identical conditions, and through 3000 scenarios, we obtained 180,000 vehicle flights for analysis.
ustainability 2023, 15, x FOR PEER REVIEW Sustainability 2023, 15, 437 16 of 21 Figure 12. Simulation scenario. Figure 12. Simulation scenario. Figure 13 shows the BQA simulation result when the OTP and ALI were altered. Except for when ALI was 20 s, when the data OTP increased, the operation OTP slightly Figure 13 shows the BQA simulation result when the OTP and ALI wer increased. However, when ALI was 25 s and 30 s, as the data OTP increased, the operation cept for whenThe OTP decreased. ALI wasin20 increase datas, OTP when the data is premised OTP on the factincreased, that 60 eVTOLthe operation aircraft increased. However, were attempting when to approach ALIspan in a time wasof25 20 smin; andtherefore, 30 s, asitthe can data OTP increased, be concluded that the flight density of eVTOL aircraft was high. Consequently, when ALI was 20 s, the OTP decreased. The increase in data OTP is premised on the fact that 60 eV vertiports’ landing capacity could cover the concentrated eVTOL aircraft flights, increasing were attempting the operation towhen OTP, but approach ALI wasin 25 a time s or 30 s, span ofworsened. the OTP 20 min; therefore, it can be co the flight density of eVTOL aircraft was high. Consequently, when ALI was 2 ports’ landing capacity could cover the concentrated eVTOL aircraft flights, i operation OTP, but when ALI was 25 s or 30 s, the OTP worsened. A similar result can be found in Figure 14, showing the simulation model. As in the BQA simulation, when ALI was 20 s, the operation OTP inc data OTP increased, but when ALI was 25 s or 30 s, the OTP decreased. M had more impact than the data OTP on both the BQA and BBQA model op This result indicates that the concentrated demand of the data OTP affects OTP, but the ALI or the vertical landing capacity is a more important elem mining the operation OTP.
Sustainability 2023, 15, x FOR PEER REVIEW 17 of 21 Sustainability 2023, 15, 437 17 of 21 Figure 13. Simulation results of BQA model with average landing interval and OTP. Figure 13. Simulation results of BQA model with average landing interval and OTP. A similar result can be found in Figure 14, showing the simulation of the BBQA model. As in the BQA simulation, when ALI was 20 s, the operation OTP increased as the data OTP increased, but when ALI was 25 s or 30 s, the OTP decreased. Moreover, ALI had more impact than the data OTP on both the BQA and BBQA model operation OTP. This result indicates that the concentrated demand of the data OTP affects the operation OTP, but the ALI or the vertical landing capacity is a more important element in determining the operation OTP.
Sustainability 2023, 15, x FOR PEER REVIEW 18 of 21 Sustainability 2023, 15, 437 18 of 21 Figure 14. Simulation results of BBQA model with average landing interval and OTP. Figure 14. Simulation results of BBQA model with average landing interval and OTP. Table 5 shows the OTP comparison chart for BQA and BBQA for each scenario. Previ- Table ously, 5 shows the we confirmed OTP that comparison BBQA shows achart betterfor OTPBQA and compared result BBQA for each scenario. to BQA. Pre- Especially viously, we confirmed that BBQA shows a better OTP result compared when the ALI was 20 s, the BBQA model showed a superior OTP performance to BQA, to BQA. Especially when the it although ALI used was 20s, the smaller BBQA model airspace. On theshowed a superior other hand, when OTP performance ALI was 25 s and to30 BQA, s, the although it used smaller airspace. On the other hand, when ALI was BQA’s OTP was better. This result occurred because BQA used a larger airspace than 25s and 30s, BBQA,the BQA’s OTP was better. This result occurred because BQA used a larger and if they used identical airspace, BBQA would show a better result, even when the ALI airspace than BBQA, and is 25 s or 30 ifs. they used identical Therefore, airspace, as the BBQA BBQA model, whichwould show a better was designed to useresult, even when a relatively small the airspace, cannot always show better OTP than BQA, it is expected to show even better rela- ALI is 25 s or 30 s. Therefore, as the BBQA model, which was designed to use a OTP tively resultssmall whenairspace, the BBQA cannot always as has airspace show largebetter as theOTP BQAthan BQA, it is expected to show model. even better OTP results when the BBQA has airspace as large as the BQA model.
Sustainability 2023, 15, 437 19 of 21 Table 5. Comparison of OTP for BQA and BBQA. Model BQA BBQA OTP 70% 75% 80% 85% 90% 70% 75% 80% 85% 90% Average 20 s 80.8 80.8 81.8 82.8 84.2 84.2 83.1 83.2 85.0 85.4 Landing 25 s 52.3 53.6 50.4 48.7 47.7 53.6 50.3 45.8 45.6 45.0 Interval 30 s 39.7 34.5 31.2 30.1 29.1 32.3 28.4 27.1 26.0 25.3 4. Conclusions and Future Work Although in this research, we suggested a scheduling technique suited for UAM and an approach control model called BBQA, proving their excellence in OTP and LOS, there are more issues to be further studied and tested. First, UAM is a concept that is not established and is in its early stage around the world, and eVTOL aircraft used in UAM do not have accurate operating specs organized. If there are technical developments on eVTOL aircraft and more specific establishment on UAM operating concepts, this research could be reinforced and be extended to a more niche and meaningful study. Secondly, in this research, the eVTOL aircraft used in UAM are assumed to be multicopter VTOL of a small category, such as the EHang 184, EHang 216, and Volocopter 2x. This assumes short-distance travel in urban areas, and if an actual UAM is operated, deeper thought should be put into operating conditions under which different types of aircraft coexist. Therefore, future studies should be conducted on UAM with a mix of various aircraft. Lastly, this research provides an approach control model for a single vertiport. However, a multivertiport should also be considered. In reality, commercial aircraft operation takes alternate airport, as well as destination airport, into consideration when performing air traffic management. If there are weather difficulties at the destination airport or capacity problems and aircraft cannot land, there is a need for traffic control that is connected to surrounding vertiports. Hence, for a more finalized and safer UAM operation, studies should be conducted taking these factors into consideration. Funding: This research was supported by the BK21 Program (5199990814084) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The author declares no conflict of interest. References 1. Straubinger, A.; Verhoef, E.T.; de Groot, H.L. Will urban air mobility fly? The efficiency and distributional impacts of UAM in different urban spatial structures. Transp. Res. Part C Emerg. Technol. 2021, 127, 103124. [CrossRef] 2. Antcliff, K.R.; Moore, M.D.; Goodrich, K.H. Silicon Valley as an Early Adopter for On-demand Civil VTOL Operations. In Proceedings of the 16th AIAA Aviation Technology, Integration, and Operation Conference, Washington, DC, USA, 13–17 June 2016; p. 3466. 3. Patterson, M.D.; Isaacson, D.R.; Mendonca, N.L.; Neogi, N.A.; Goodrich, K.H.; Metcalfe, M.; Bastedo, B.; Metts, C.; Hill, B.P.; DeCarme, D.; et al. An Initial Concept for Intermediate-State, Passenger-Carrying Urban Air Mobility Operations. In Proceedings of the AIAA Scitech 2021 Forum, Virtual, 11–15 & 19–21 January 2021; p. 1626. 4. AIRBUS. Vahana Has Come to an End. But a New Chapter at Airbus Has Just Begun. 2019. Available online: https://www.airbus. com/en/newsroom/stories/2019-12-vahana-has-come-to-an-end-but-a-new-chapter-at-airbus-has-just-begun (accessed on 15 October 2022). 5. EHANG. The Future of Transportation: White Paper on Urban Air Mobility Systems. 2020. Available online: https://www.ehang. com/app/en/EHang%20White%20Paper%20on%20Urban%20Air%20Mobility%20Systems.pdf (accessed on 15 October 2022). 6. BOEING. Boeing Autonomous Passenger Air Vehicle Completes First Flight. 2019. Available online: https://boeing.mediaroom. com/2019-01-23-Boeing-Autonomous-Passenger-Air-Vehicle-Completes-First-Flight (accessed on 15 October 2022).
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