Optimal Vertiport Airspace and Approach Control Strategy for Urban Air Mobility (UAM)

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 management 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.

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 that can alleviate traffic congestion, make streets safer, and reduce air pollution [2]. NASA defines UAM as a subset of advanced air mobility (AAM) and expects applications in a variety of fields, such as emergency patient transport, cargo transport, and passenger transport [3]. To make UAM a reality, numerous venture companies, aviation manufacturers, 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 [4,5,6,7,8,9]. 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 of Operations v1.0”, which provides an initial foundational perspective supporting the introduction and incorporation of UAM operations into the National Airspace System (NAS) [10]. In addition, to materialize this concept, NASA formed the AAM (Advanced 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 infrastructure construction were undertaken by announcing the “Engineering Brief No. 105, Vertiport Design”, which contains guidelines for vertiport design, and related laws are planned to be enacted by 2024 [12]. Europe is also preparing for the implementation of UAM by announcing “NPA 2022-06”, which contains relevant certification regulations and comprehensive regulations for UAM operation—the first such regulations in the world [13]. In industry, discussions continue on the introduction of digitally based ATM (air traffic management) and the necessary systems and procedures for vertiport operation [14,15]. As such, preparations for the realization of UAM are underway in various stages around the world, especially in advanced aviation countries.

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 operation 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 suggested 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 considering 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 [31,32,33]. 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 vertiport 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 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 determine 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 previous 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 queuing approach (BBQA) model, which provides identical airspace safety, as well as the OTP performance of an SBA model.

The proposed BBQA model is based on the BQA model and is a concept that allows for freer movement between holding points to improve the OTP. As shown in Figure 1, whereas the BQA model strictly regulates branches, which connect the holding points, the BBQA is a more flexible model that allows for movement when there is no possibility of collision between eVTOL aircraft. The airspace design concept of the BBQA model minimizes the bottleneck phenomenon encountered in the BQA model, which is caused by the occupation of the holding point of the preceding aircraft in the same queue. Therefore, the proposed model is expected to minimize the landing sequence reversal phenomenon.

Figure 1. The concepts of the balanced branch queuing approach.

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 models as verified by simulation experiments.

2.1. BBQA Airspace Design Concept

To deduce the optimal airspace design for the BQA model, we suggested a new algorithm that combines the methods of the SBA and BQA. The existing BQA model had a restriction that limited the branches that connect the holding points to only integer multiples. However, due to this restriction, even when it had a lower capacity, the problem of additional required airspace occurred, as shown in the previous research [36]. The BBQA’s optimal airspace searching process combined the SBA process and BQA process, as shown in Figure 2. In the BBQA model, the branch connection between holding points is not required to be integer multiples nor to have the identical branch queuing shape. Therefore, based on the airspace design of the SBA’s holding point arrangement, a new algorithm was developed that can connect the branches like the BQA.

Figure 2. BBQA airspace design concept: Hybrid of SBA and BQA.

The first step of designing the airspace for BBQA is identical to the SBA. First, find the radius of the holding circle and the optimal holding point arrangement based on the algorithm suggested in the previous study [35]. The second step is to arrange the optimal branch in the airspace determined by methods identical to the SBA. The key to arranging the optimal branch is to find the optimal pair between the holding points that are in the two adjacent holding circles, as shown in Figure 3. In other words, the adjacent holding point should be able to be connected without causing any collision for all pairs that can be moved. As shown in Figure 3, if there are $n_1$ holding points in the inner holding circle and there are $n_2$ holding points in the outer holding circle, it can be altered to a problem to find the optimal link that connects $n_1+n_2$ nodes. If the shortest $n_1+n_2$ paths out of $n_1\times n_2$ paths are selected, the optimal combination can be found where no collision between branches occurs, even when the adjacent holding points are connected. Therefore, in this research, we performed steps to produce a distance matrix between nodes and to find the shortest $n_1\times n_2$ number of paths in the distance matrix.

Figure 3. Branch design of BBQA.

2.2. Algorithm for the BBQA Airspace

The algorithm to find BBQA model’s optimal airspace was upgraded based on the algorithm sin the previous study [35]. Identical to SBA and BQA, as it was more important to find an exact design than having a faster solving speed, more focus was put on finding the exact solution through a full search rather than using heuristic methods. Algorithm 1 shows the algorithm’s pseudocode to search for the optimal airspace of BBQA. Through the given algorithm, the most optimal holding point arrangement, holding circle radius, and branch design for BBQA can be determined.

Algorithm 1. Pseudocode for determining the optimal airspace algorithm for BBQA

Given: $\hspace{1em}{t}_h$: Max holding time $\hspace{1em}{\Delta }_S$: Min time separation between takeoff and landing $\hspace{1em}{d}_u$: Minimum separation distance between eVTOL aircrafts. Initialize:  K = 1 // Initial number of holding circles $\hspace{1em}{r}_{opt}=500$ // Initial optimal radius of the outermost holding circle $\hspace{1em}{C}_t=\frac{t_h}{2\times {\Delta }_S}$ // Calculate maximum airspace capacity of VTCA. Repeat  Find the combination of $n_i$ satisfying constraint $\sum n_i=C_t$ FOR All combinations of $n_i$  $r_i=MAX\left[\frac{d_u}{2\times \sin \frac{180°}{n_i}},r_{i-1}+d_u\right]$ // Calculate the radius of each holding circle $i$. $\hspace{1em}{r}_{out}=r_k$ // Radius of the outermost holding circle END FOR IF $r_{opt}\ge MIN\left[r_{out}\right]$ THEN  $\hspace{1em}{r}_{opt}=MIN\left[r_{out}\right]$  $\hspace{1em}k= k+1$ // 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 $n_i$ of holding circle $i$   FOR y = 1 to $n_{i+1}$ of holding circle $i+1$ DIST(x, y) = distance between two points // Calculate distance matrix   END FOR  END FOR  Find the $n_i+n_{i+1}$ 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:

$$\begin{array}{l}Min r_{outer}\\ s.t{r}_i\ge \frac{d_u}{2\times \sin \frac{180°}{n_i}}\\ r_i\ge r_{i-1}+d_u\\ \sum n_i=C_t\\ 0\le n_i\le C_t,n_{i }\mathrm{is} \mathrm{integer} \left(n_i\in \mathbb{Z}\right)\end{array}$$

where $r_i$: the radius of each holding circle ($i$);

$n_i$: the number of holding points on holding circle i;

$d_u$: minimum separation distance between eVTOL aircraft;

$C_t$: 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.

Figure 4. Various airspace designs of BBQA with the same radius.

2.3. Simulation Results

We conducted a simulation to determine the optimal airspace for the BBQA strategy. Figure 5 shows the simulation result for each model and the optimal airspace model following the number of holding circles. The radius of the outermost holding circle decreased when the holding circle increased from one to four for SBA, BQA, and BBQA, but when there were five holding circles, the radius increased, producing an inefficient airspace design. In addition, the BQA needed more airspace than SBA or BBQA, as it has restrictions on its branches. The newly introduced BBQA requires identical airspace as SBA but also finds the optimal branch shape between holding points. BQA increased the airspace safety by restricting the paths of eVTOL aircraft with branches with the airspace design but required significantly larger airspace than SBA and proved to be inefficient in terms of space efficiency. As shown in Figure 5, if there are two holding circles, BQA requires an airspace radius of 115.7 m, whereas SBA requires an airspace radius of 106.1 m. In all cases, with three to five holding circles, it can be seen that the required airspace of BQA is larger than that of SBA. BBQA used the same airspace as SBA and achieved the same airspace safety as BQA.

Figure 5. Airspace design for each model under the same conditions. (1), (2),... (5) means the number of holding circles.

To check whether the suggested algorithm can find the optimal airspace effectively under various conditions, the parameters that affect the airspace design were altered. The simulation was performed with the max holding time $\left(t_h\right)$ assumed to be 1200 s, the minimum time separation between takeoff and landing (${\Delta }_S$) assumed to be 5~15 s, and the minimum separation distance between eVTOL aircraft ($d_u$) in the range of 20~40 m. The simulation result is shown in Figure 6. Through this result, it can be seen that even with various changes in conditions, the optimal airspace search for BBQA performs well. In addition, it can be seen that the shape of the airspace is determined by ${\Delta }_S$. When ${\Delta }_S$ is constant, the airspace changes in size depending on the $d_u$, but the branch shape and holding point arrangement remain identical. ${\Delta }_S$ determines the maximum airspace capacity of VTCA and therefore affects the airspace shape, but $d_u$ only affects the size and not the shape.

Figure 6. Various airspace designs of BBQA with parameter changes.

3. Approach Control Using BBQA Model

3.1. Development of BBQA Model

The BBQA model operates similarly to the BQA model proposed in our previous research [36]. However, in the airspace design stage, it is not restricted in its branch count, with increased in space efficiency by using SBA’s airspace. Figure 7 shows the approximate framework of the BBQA model. The BBQA model consists of two stages similar to existing models. The first step is a preparation step for the implementation of the model. In the first step, the parameters necessary for airspace design and approach control are inserted, and based on this input, the optimal airspace, which includes the radius of the holding circle, the holding point arrangement, and branch count, is constructed. In addition, the basic initialization, whereby the flight data are loaded and the initial ETA (estimated time of arrival) is calculated, is performed.

Figure 7. The framework of the BBQA model.

During the second step, real approach control is performed. In the flight status data, all information required for approach control, such as the coordinates of the vehicles, landing sequence, and vehicle occupancy of holding points, is included and checked and updated in real time. If landing at a vertiport is possible, then a search is performed for vehicles that can land in holding circle 1. If there is such a vehicle, then it gives landing clearance to the aircraft with the highest priority in the landing sequence. At the same time, it searches for a vehicle that can enter the empty holding point, which becomes unoccupied as the aircraft that are granted landing clearance in holding circle 1 begin the landing sequence. Unlike BQA, because BBQA is connected with diverse branches, a vehicle with the fastest sequence is chosen among the eligible vehicles. This process is identical all the way to holding circle N. The vehicles that are granted landing or moving clearance move through a predetermined route. Additionally, they update the landing sequence of the flight status data in real time by performing GA-based optimal scheduling.

The biggest improvement of the BBQA model compared to the existing BQA is the reduction of the bottleneck phenomenon by designing a more flexible branch structure. If the bottleneck phenomenon is decreased, the occurrence of landing reversal is also decreased, and the landing sequence calculated through schedule optimization can maintained.

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 26, not 36. However, the BBQA grants moving clearance to eVTOL number 26, which has a faster landing sequence. Through this procedure, the landing sequence control efficiency can be maximized.

Figure 8. A sample of BBQA model application.

3.2. Advantages of the BBQA Model

To check the quality of the suggested BBQA model, a series of simulation experiments were performed. To compare it with the existing BQA, SBA, and SBAM models, we performed a total of 200 simulations: 100 different flight schedules and scenarios using 2 different scheduling strategies. We analyzed the 12,000 vehicle flight data points deduced from this scenario. In addition, we compared the result to the existing models and checked the performance of the BBQA.

Figure 9 is a histogram of BQA and BBQA’s delay times. To check the excellence of the suggested BBQA model, a series of simulation experiments were performed. The delay time was calculated according to the difference between the actual time of arrival (ATA) and the scheduled time of arrival (STA); if this value is negative, it signals an early arrival. The delay time distribution difference between BQA and BBQA was not very significant. However, the BBQA model, similarly to existing models, had delay times closer to zero when using the genetic-algorithm-based scheduling model compared to when using the first-come-first-served (FCFS) model. Accordingly, it can be confirmed that the scheduling strategy that minimizes the deviation of STA and ATA is implemented well in the BBQA model.

Figure 9. Delay time for BQA and BBQA models.

As another indicator that can check the excellence of BBQA, the punctuality was calculated as follows. In this study, it was assumed that arriving within 2 min of the planned schedule was on time.

$$\mathrm{On}-\mathrm{Time} \mathrm{Performance} (\mathrm{OTP})=\frac{{{\displaystyle \sum }}_{i=1}^{N_V}{x}_i}{N_V}\times 100,$$

where$x_i=\left\{\begin{array}{c}1, \mathrm{if} AT{A}_i\le ST{A}_i+2\\ 0, \mathrm{otherwise} \end{array}\right.$;

$N_v$: the number of UAM eVTOL aircraft.

Punctuality is an immensely important element not only in commercial airlines but also in UAM. UAM has to operate a vertiport inside a limiting urban space, and if operated as air taxis, the link between other forms of transportation is important; therefore, the arrival punctuality has great significance. Therefore, in UAM approach control, OTP (on-time performance) is an index that can prove the excellence of models. Figure 10 shows the OTP result for each model with a comparison of OTP for FCFS and GA-based scheduling. As in the results shown in Figure 10, the OTP of all models 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.

Figure 10. On-time performance results for each model.

A similar result can be found in the landing sequence reversal occurrence status. The BQA and BBQA models restrict vehicle movement strictly to branches and show a bottleneck phenomenon due to queuing in identical branches. The bottleneck phenomenon 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 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 perfectly. In other words, it searched for a more efficient airspace than in the airspace design step. Therefore, the simulation began in a smaller airspace than the BQA model, as in Table 1 and Figure 11. The holding point arrangement showed a different shape to that of BQA as well. However, BBQA_B used BQA’s holding point arrangement and radius of the holding circle and used BBQA’s branch design. This was compared for both cases because it is not possible to compare how approach control purely effects landing sequence reversal if the airspace size and holding point arrangements are different.

Table 1. Holding circles for comparison of landing sequence reversal.

	Radius (m)				# of Holding Points
Holding Circle	1	2	3	4	1	2	3	4
BQA	32.4	52.4	72.4	92.4	10	10	20	20
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

Figure 11. Airspace design for comparison of landing sequence reversal.

As reflected by the results presented in Table 2, there was an average of 3.8 landing sequence reversal and 3 instances which showed 8 reversal phenomena in the BQA model. In comparison, the BBQA_A showed an average of 5.8 reversal phenomena, which is more than the BQA model. However, as shown in Figure 11, it is unreasonable to directly compare it with BQA, as the result was derived from an airspace different than that in the BQA mode. It is also shown in Figure 11 that although there were more reversal phenomena, the OTP improved significantly. The result of BBQA_B, which was conducted under identical airspace conditions as BQA, showed a definite decrease in reversal phenomena. There was an average of 2.76 reversals and even 4 cases in which no reversal occurred. In an airspace identical to BQA, the BBQA model showed an improvement in landing sequence reversal.

Table 2. Comparison results of landing sequence reversal.

Landing Sequence Reversal	BQA	BBQA_A	BBQA_B
0	-	-	4
1	8	-	16
2	17	-	28
3	24	4	21
4	17	12	16
5	17	25	13
6	10	23	2
7	4	29
8	3	6
9	-	1
Total	100	100	100

In this study, we also compared airspace safety using the LOS (loss of separation) concept. LOS indicates that the distance between two aircraft is not secured by the minimum safety distance [37]. In addition, operational error (OE) is when LOS occurs due to an air traffic control mistake, as defined by the FAA’s Air Traffic Organization (ATO) and is classified into four hazardous stages. Table 3 shows the occurrence status and occurrence ratio of the OE’s danger stage for each model. Identical to the BQA model, the BBQA model only enabled travel on 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.

Table 3. Comparison of airspace safety for each model.

OE Severity	BQA /BBQA		SBA		SBAM
	FCFS	GA	FCFS	GA	FCFS	GA
Proximity Events (PE)	-	-	3139 (1.77%)	3152 (1.78%)	69 (0.04%)	64 (0.04%)
Low Risk (LR)	-	-	2234 (1.26%)	2093 (1.18%)	378 (0.21%)	369 (0.21%)
Moderate Risk (MR)	-	-	-	-	1151 (0.65%)	1142 (0.65%)
High Risk (HR)	-	-	-	-	983 (0.56%)	835 (0.47%)
OE Total	-	-	5373 (3.04%)	5245 (2.96%)	2581 (1.46%)	2410 (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 scenarios, 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
$ST{A}_i$ (scheduled time of arrival)	$U\left(1, 20 \right)$
$t_h$(max holding time)	1200 s
${\Delta }_S$ (min time separation between takeoff and landing)	10 s
$C_t$ (max airspace capacity of VTCA)	60 vehicles
$S_v$ (vertical speed of approach)	2.5 m/s
$S_c$ (vertical speed of approach)	3 m/s
$d_u$ (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 ${\Delta }_S$ 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.

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 increased. However, when ALI was 25 s and 30 s, as the data OTP increased, the operation OTP decreased. The increase in data OTP is premised on the fact that 60 eVTOL aircraft were attempting to approach in a time span of 20 min; therefore, it can be concluded that the flight density of eVTOL aircraft was high. Consequently, when ALI was 20 s, the vertiports’ landing capacity could cover the concentrated eVTOL aircraft flights, increasing the operation OTP, but when ALI was 25 s or 30 s, the OTP worsened.

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.

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. Previously, we confirmed that BBQA shows a better OTP result compared to BQA. Especially when the ALI was 20 s, the BBQA model showed a superior OTP performance to BQA, although it used smaller airspace. On the other hand, when ALI was 25 s and 30 s, the BQA’s OTP was better. This result occurred because BQA used a larger airspace than BBQA, and if they used identical airspace, BBQA would show a better result, even when the ALI is 25 s or 30 s. Therefore, as the BBQA model, which was designed to use a relatively small airspace, cannot always show better OTP than BQA, it is expected to show even better OTP results when the BBQA has airspace as large as the BQA model.

Table 5. Comparison of OTP for BQA and BBQA.

Model		BQA					BBQA
OTP		70%	75%	80%	85%	90%	70%	75%	80%	85%	90%
Average Landing Interval	20 s	80.8	80.8	81.8	82.8	84.2	84.2	83.1	83.2	85.0	85.4
	25 s	52.3	53.6	50.4	48.7	47.7	53.6	50.3	45.8	45.6	45.0
	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.