Repurposing Existing Infrastructure for Urban Air Mobility: A Scenario Analysis in Southern California
重用现有基础设施用于城市空中移动：南加州的情景分析

Abstract 摘要

The deployment of urban air mobility in built-out metropolitan regions is constrained by infrastructure opportunities, land use, and airspace zoning designations. Meanwhile, the availability and spatial distribution of infrastructure opportunities influence the travel demand that can be potentially captured by UAM services. The purpose of this study is to provide an initial assessment of the infrastructure opportunities of UAM in southern California with different mixes of spatial constraints, such as noise levels, school buffer zones, and airspace zones. The corresponding travel demand that can be potentially captured under each scenario is estimated with a home–workplace trip table. The results of the analyses indicate that supply-side infrastructure opportunities, such as heliports and elevated parking structures, are widely available to accommodate the regional deployment of UAM services. However, current spatial constraints can significantly limit the scope of vertiport location choices. Furthermore, the low-income population, blue-collar workers, and young people live farther away from supply-side opportunities than the general population. Moreover, this study proposes a network of UAM based on the top home-based and workplace-based stations for long-distance trips.
在已建成的大都市地区部署城市空中移动受到基础设施机会、土地利用和空域划分的限制。与此同时，基础设施机会的可用性和空间分布影响着城市空中移动服务可能捕获的出行需求。本研究的目的是对南加州城市空中移动的基础设施机会进行初步评估，考虑到不同的空间限制，如噪音水平、学校缓冲区和空域区域。通过家庭-工作地出行表，估计了在每种情景下可能捕获的出行需求。分析结果表明，供给方面的基础设施机会，如直升机停机坪和高架停车设施，广泛可用以适应城市空中移动服务的区域部署。然而，当前的空间限制可能会严重限制垂直港口的选择范围。此外，低收入人群、蓝领工人和年轻人与一般人口相比，离供给方面的机会更远。 此外，该研究提出了一个基于家庭和工作场所站点的 UAM 网络，用于长途出行。

1. Introduction 介绍

The transportation sector faces the challenge of meeting the growing demand for convenient passenger mobility while reducing congestion, improving safety, and mitigating emissions. Automated driving and electric propulsion are disruptive technologies that may contribute to these goals, but they are still limited by congestion on existing roadways and land-use constraints. Urban air mobility (UAM) can positively contribute to a multimodal mobility system by leveraging the sky to better link people to cities and regions. Commercial UAM operations have begun in the United States since at least the 1940s. For example, from 1947 to 1971, Los Angeles Airways used helicopters to transport people and mail between dozens of locations in the Los Angeles basin, including Disneyland and Los Angeles International Airport [1,2]. During the same era, from 1949 to 1979, New York Airways primarily used helicopters to fly people between helipads in Manhattan and airports in the New York area, such as LaGuardia, JFK, and Newark. Tragically, these initial commercial experiments experienced several fatal accidents. After those accidents, the once-booming operation of helicopter-based UAM was halted nationwide, and the resulting financial difficulties led these companies to cease operations [3]. Despite these historical operation failures, recent technological advances provide an opportunity for the resurgence of urban air mobility.
交通部门面临着满足不断增长的便捷乘客出行需求、减少拥堵、提高安全性和减少排放的挑战。自动驾驶和电动推进是可能有助于实现这些目标的颠覆性技术，但它们仍受到现有道路拥堵和土地利用限制的限制。城市空中移动（UAM）可以通过利用天空更好地将人们与城市和地区联系起来，积极促进多式联运系统。自 20 世纪 40 年代以来，美国已经开始了商业 UAM 运营。例如，从 1947 年到 1971 年，洛杉矶航空公司使用直升机在洛杉矶盆地的数十个地点之间运送人员和邮件，包括迪士尼乐园和洛杉矶国际机场[1, 2]。在同一时期，从 1949 年到 1979 年，纽约航空公司主要使用直升机在曼哈顿的直升机停机坪和纽约地区的机场之间运送人员，如拉瓜迪亚、肯尼迪和纽瓦克。不幸的是，这些最初的商业实验经历了几起致命事故。 在那些事故发生后，曾经蓬勃发展的基于直升机的城市空中移动运营在全国范围内停止，由此导致的财务困难使这些公司停止运营[3]。尽管存在这些历史性的运营失败，但最近的技术进步为城市空中移动的复兴提供了机会。

According to NASA [4], the electric vertical takeoff and landing (eVTOL) UAM system has reached “a level of maturity to enable UAM using safe, quiet, and efficient unmanned vehicles to conduct on-demand and scheduled operations (p. 1)”. Types of UAM operations could be emergency responses, humanitarian missions, newsgathering, package delivery, and passenger transport. Among these potential applications, the use of passenger transport is of the greatest attention given its promising future to alleviate traffic congestion with green technology at a new dimension. For instance, a study in the Bay Area has provided evidence that the UAM system would have a significant impact on reducing the trip time for trips greater than 15 miles [5]. In addition, fully loaded eVTOL’s greenhouse gas emissions per passenger-kilometer are less than half of internal combustion engine vehicles and 6% lower than ground electric vehicles [6].
根据 NASA 的说法[4]，电动垂直起降（eVTOL）的城市空中交通（UAM）系统已经达到了“使无人机安全、安静、高效地进行按需和定期操作的成熟水平（第 1 页）”。UAM 操作的类型可以包括紧急响应、人道主义任务、新闻采集、包裹投递和乘客运输。在这些潜在应用中，乘客运输的使用受到了最大的关注，因为它有望以全新的维度利用绿色技术缓解交通拥堵问题。例如，湾区的一项研究提供了证据，表明 UAM 系统将显著减少超过 15 英里的行程时间[5]。此外，满载的 eVTOL 每公里乘客排放的温室气体比内燃机车辆少一半，比地面电动车辆低 6%[6]。

Despite the technological advances and growing commercial interest, it is important to note that the adoption of UAM still faces multiple challenges, as highlighted by many researchers. Such challenges include estimating the demand for air taxi services (transportation modeling and market analysis), air traffic control, operation, infrastructure planning, safety, and regulations [4,7,8,9,10]. One of the most critical issues for scaling UAM in built-out metropolitan regions is identifying appropriate landing sites along with other supply-side opportunities and constraints, such as no-fly zones, noise levels, and school zones, to accommodate the regional-wide deployment of eVTOLs [11,12,13].
尽管技术进步和商业兴趣不断增长，但需要注意的是，采用城市空中交通（UAM）仍面临多重挑战，正如许多研究人员所强调的。这些挑战包括估计空中出租车服务的需求（交通建模和市场分析）、空中交通管制、运营、基础设施规划、安全和法规[4, 7, 8, 9, 10]。在已建成的大都市地区扩大城市空中交通的最关键问题之一是确定适当的着陆点，以及其他供应方机会和限制，如禁飞区、噪音水平和学校区域，以适应电动垂直起降飞行器在整个地区的部署[11, 12, 13]。

To identify the infrastructure opportunities and assess the impact of spatial constraints on these opportunities, this study (1) explores the availability and spatial distribution of current urban infrastructure that can be potentially used as UAM landing sites or vertiports; (2) conducts scenario analyses on how various spatial constraints affect the infrastructure opportunities of UAM; and (3) investigates how different scenarios affect the accessibility of vertiports for different groups of home–workplace commuters.
为了确定基础设施机会并评估空间限制对这些机会的影响，本研究(1)探讨了当前城市基础设施的可用性和空间分布，这些基础设施可以潜在地用作无人机空中交通着陆点或垂直起降场；(2)对各种空间限制如何影响无人机空中交通基础设施机会进行了情景分析；(3)研究了不同情景如何影响不同群体的居住地-工作地通勤者对垂直起降场的可达性。

The paper proceeds as follows: Section 2 presents the literature review. Section 3 describes the data collection process and scenario analysis framework. Section 4 presents the results of the scenario analysis, and Section 5 discusses the conclusion, implications, and limitations of this study.
本文的内容如下：第 2 节介绍了文献综述。第 3 节描述了数据收集过程和情景分析框架。第 4 节介绍了情景分析的结果，第 5 节讨论了本研究的结论、意义和局限性。

2. Literature Review 文献综述

Although urban air mobility (UAM) is an emerging mode of transportation, many studies have been conducted to understand the factors associated with transportation technology acceptance. In particular, the social acceptance and user perceptions of ground autonomous vehicles (GAVs) have received increasing scholarly attention in recent years. In the case of UAM, examining recurring factors in relevant studies of ground autonomous vehicles and on-demand aerial service provides valuable insights into the future of UAM adoption, given that only a few pioneering studies have focused on users’ preferences for UAM.
尽管城市空中移动（UAM）是一种新兴的交通方式，但已经进行了许多研究来了解与交通技术接受相关的因素。特别是，近年来，对地面自动驾驶车辆（GAVs）的社会接受和用户感知引起了越来越多的学术关注。在 UAM 的情况下，研究地面自动驾驶车辆和按需空中服务的相关研究中的重复因素，可以为 UAM 采用的未来提供有价值的见解，因为只有少数先驱性研究关注了用户对 UAM 的偏好。

2.1. Demand-Side Factors Associated with UAM or GAV Adoption
与城市空中交通（UAM）或通用航空器（GAV）采用相关的需求方面因素。

Increasing scholarly and institutional attention has been paid to understanding factors associated with the adoption of autonomous transportation technologies. In recent years, social barriers and key factors related to user adoption have been widely studied for ground autonomous vehicles. Although GAVs are different from UAM from a technological perspective, they share many characteristics in user adoption from the innovation diffusion perspective. Sociodemographic factors, such as income level and gender, were significantly associated with autonomous vehicle acceptance [14,15]. Higher-income, technology-savvy males who live in urban areas and those who have experienced more car accidents (risk takers) have a greater interest in and higher willingness to pay for a GAV [14]. User choice is also influenced by social networks, including neighbors and close friends. Ref. [16] pointed out that users’ perceptions of risks hinder the adoption of GAVs (e.g., data privacy and remote hacking). While trust is the most critical factor related to GAV acceptance, perceived ease of use and perceived usefulness are also significantly related to innovation adoption [17]. While GAVs hold the promise of reducing the number of crashes and fatalities on the roads, safety concerns are one of the major barriers to promoting the adoption of GAVs [18].
越来越多的学者和机构开始关注与自动驾驶技术采用相关的因素。近年来，对地面自动驾驶车辆的社会障碍和用户采用的关键因素进行了广泛研究。虽然从技术角度来看，地面自动驾驶车辆与城市空中交通（UAM）不同，但从创新扩散的角度来看，它们在用户采用方面有许多共同特点。社会人口因素，如收入水平和性别，与自动驾驶车辆的接受度显著相关[14, 15]。高收入、技术熟练的男性、居住在城市地区以及经历过更多车祸（冒险者）的人对地面自动驾驶车辆更感兴趣，愿意支付更高的价格[14]。用户的选择也受到社交网络的影响，包括邻居和亲密朋友。参考文献[16]指出，用户对风险的感知阻碍了地面自动驾驶车辆的采用（例如数据隐私和远程黑客攻击）。尽管信任是与地面自动驾驶车辆接受度最关键的因素，但感知易用性和感知有用性也与创新采用显著相关[17]。 虽然自动驾驶车辆有望减少道路上的事故和死亡人数，但安全问题是推广自动驾驶车辆采用的主要障碍之一[18]。

Interestingly, these concerns about GAV adoption reappear in early studies on UAM adoption. In a stated preference survey study in Germany, ref. [19] explored user perception on UAM. The study indicated that safety and trust are primary concerns for UAM adoption, and adopters are younger and have higher incomes. Ref. [20] extended the study of UAM adoption to Munich, Germany, and their results further suggested that younger individuals and older populations with higher household incomes are more likely to adopt UAM. Moreover, trip purpose proved to be a significant consideration, with noncommuting travel being the respondents’ most preferred GAV option. Airbus’s survey [21] in four different countries reported that communities are most concerned about safety followed by the type of sound generated from the aircraft and then the volume of sound from the aircraft. Less than half (44%) of all respondents’ initial reactions to UAM are in support or strong support while 41% of all respondents believe UAM is either safe or very safe. Deloitte’s report provided similar insights that demographic factors are significantly associated with UAM adoption as younger generations (Gen Y and Gen Z) are more likely to agree that UAM provides an efficient alternative mode of urban transportation [22]. Table 1 presents a summary of recent studies on factors associated with UAM or AV adoption.
有趣的是，关于 GAV 采用的这些担忧在早期的 UAM 采用研究中再次出现。在德国的一项声明偏好调查研究中，参考文献[19]探讨了用户对 UAM 的感知。研究表明，安全和信任是 UAM 采用的主要关注点，采用者年龄较小且收入较高。参考文献[20]将 UAM 采用的研究扩展到德国慕尼黑，他们的结果进一步表明，年轻人和收入较高的老年人更有可能采用 UAM。此外，出行目的被证明是一个重要的考虑因素，非通勤旅行是受访者最喜欢的 GAV 选项。空客在四个不同国家的调查[21]报告称，社区最关注的是安全，其次是飞机产生的声音类型，然后是飞机的声音大小。不到一半（44%）的受访者对 UAM 的初步反应是支持或强烈支持，而 41%的受访者认为 UAM 要么安全，要么非常安全。 德勤的报告提供了类似的见解，即人口统计因素与 UAM 采用率显著相关，因为年轻一代（Y 世代和 Z 世代）更有可能认同 UAM 提供了一种高效的城市交通替代方式[22]。表 1 总结了与 UAM 或 AV 采用相关的最新研究。

Table 1. Recent demand-side key studies on UAM adoption.

Authors	Year of Publication	Area of Research	Study Area	Key Findings
Al Haddad et al.	2020	UAM	Germany	(1) Safety and trust, affinity to automation, data concerns, social attitude, and sociodemographics are important issues for UAM adoption; (2) skeptical respondents had behavior similar to late and nonadopters [19].
Bansal et al.	2016	GAV	Austin, Texas	(1) Higher-income, technology-savvy males who live in urban areas and those who have experienced more crashes have a greater interest in and higher willingness to pay for AV; (2) user choice is dependent on friends’ and neighbors’ adoption rates [14].
Fu et al.	2019	UAM	Munich, Germany	(1) Travel cost and safety may be critical determinants in UAM adoption; (2) younger individuals as well as older individuals with high household income are more likely to adopt UAM; 3) during the market entry stage, potential travelers may favor UAM particularly for performing noncommuting (recreational flying) trips [20].
Roy et al.	2019	UAM	U.S. (CSAs)	(1) Near-term eVTOL aircrafts may be able to dramatically increase the expected user base compared to present-day helicopters flying the same mission; (2) assumptions including vehicle design range, payload, and cruising speed can change the results significantly [21].
Hohenberger et al.	2016	GAV	Germany	(1) The anxiety level has a significant gender-related difference towards AV adoption; (2) differential effect of sex on anxiety was more pronounced among relatively young respondents and decreased with participants’ age [15].
Kyriakidis et al.	2015	GAV	109 countries	(1) Most people think manual driving is still the most enjoyable mode of travel; (2) respondents were found to be most concerned about software hacking/misuse and were also concerned about legal issues and safety; (3) respondents from more developed countries were less comfortable with their vehicle transmitting data [16].
Lidynia et al.	2016	Drones	Aachen University, Germany	(1) Laypeople feared the violation of their privacy whereas active drone pilots saw more of a risk of possible accidents; (2) participants had clear expectations regarding the routes drones should and should not be allowed to use [23].
Deloitte	2019	UAM	20 countries	(1) Despite the substantial progress made in terms of vehicle design and technology, consumers continue to doubt the safety of UAM; (2) regional and generational differences play a critical role in the perceived UAM safety; (3) 49% of respondents in the United States were unconvinced about UAM safety while only 39% are skeptical about safety in China; (4) younger consumers surveyed (Gen Y and Gen Z) agree that UAM provides for an efficient alternative mode of urban transportation but are more apprehensive about its safety [22].
Peeta et al.	2008	On-demand air service (ODAS)	Indiana, Illinois, and Florida	(1) Travel distance, service fare, and the ODAS location are key factors influencing user switching decisions (from traditional air service to ODAS); (2) ODAS landing location is a key determinant of its operational viability and has significant implications for policymakers, regional/city planners, operators, and businesses; (3) while ODAS proximity to work is an attractive attribute for users, environmental concerns can arise if close to residential areas [24].
Airbus	2019	UAM	Los Angeles, Mexico City, New Zealand, and Switzerland	(1) Communities are most concerned about safety followed by the type of sound generated from the aircraft and then the volume of sound from the aircraft; (2) other concerns include the time of day at which aircrafts are flown and the altitude at which aircraft fly; (3) 44.5% of all respondents’ initial reactions to UAM is in support or strong support while 41.4% of all respondents believe UAM is either safe or very safe [21].
Zhang et al.	2019	GAV	China	(1) Trust was the most critical factor in promoting AV acceptance; (2) perceived ease of use (PEOU) and perceived usefulness (PU) were significant factors; (3) the effects of PEOU and PU were weaker compared to trust [17].

2.2. Supply-Side Factors Related to UAM Adoption
2.2. 与 UAM 采用相关的供给侧因素

While demand-side factors have attracted lots of scholarly attention in early studies on UAM, the identification of viable UAM landing sites and other supply-side constraints/opportunities are preconditions to advance empirical studies on UAM adoption. Uber, Airbus, and other pioneers in the UAM industry have provided their archetypes of UAM infrastructure, some of which are based on existing urban infrastructure, such as elevated parking structures, parking lots, and airport terminals (Figure 1). Recent studies point out that early UAM vertiports can leverage existing airport infrastructure given the high customer willingness to pay and substantial time-savings for long-distance trips [25]. Refs. [26,27] suggested that vertiports can leverage underutilized urban infrastructures, such as helicopter pads, barges over water, inside highway cloverleaves, and qualified rooftops (e.g., parking structures), with the constraints of other supply-side, such as air space zones, land use regulations, and population density. These findings coincide with early studies (e.g., see [24]) on on-demand aerial service (ODAS), which suggested that landing location is a key determinant of its operational viability. While an ODAS’s proximity to work is an attractive attribute for users, environmental concerns can arise if it is close to residential areas. Ref. [27] emphasized that median income distribution, land value, and job density should be considered in the placement of UAM landing sites. Ref. [28] identified another research trajectory in which UAM vertiports served as a transition hub for air travelers between long-distance air travel and short/medium-distance air travel. In his work, vertiport placement was optimized based on airport traveler data. Moreover, the operational capacities of ground infrastructure play an important role in improving the operation efficiency of UAM systems. Ref. [26] suggests that balancing the number of gates and the number of vehicles will maximize the utility of UAM mode when vehicle specifications are held constant.
在早期对城市空中交通（UAM）的研究中，需求方面的因素引起了许多学者的关注，但要推进对 UAM 采用的实证研究，必须先确定可行的 UAM 着陆点和其他供给方面的限制/机会。Uber、Airbus 和其他 UAM 行业的先驱者提供了他们对 UAM 基础设施的原型，其中一些基于现有的城市基础设施，如高架停车场、停车场和机场航站楼（图 1）。最近的研究指出，早期的 UAM 垂直港口可以利用现有的机场基础设施，因为顾客愿意支付高额费用，并且长途旅行可以节省大量时间[25]。参考文献[26, 27]建议，垂直港口可以利用未充分利用的城市基础设施，如直升机停机坪、水上驳船、高速公路立交桥内部和合格的屋顶（如停车场结构），同时还要考虑其他供给方面的限制，如空域区域、土地利用规定和人口密度。这些发现与早期的研究相吻合（例如...参考[24]的研究指出，着陆位置是决定按需空中服务（ODAS）运营可行性的关键因素。虽然 ODAS 靠近工作地点对用户来说是一个吸引人的特点，但如果靠近居民区，可能会引发环境问题。参考文献[27]强调，在选择 UAM 着陆点时应考虑中位数收入分布、土地价值和就业密度。参考文献[28]确定了另一条研究轨迹，即 UAM 垂直港口作为长途航空旅行和短/中程航空旅行之间的过渡枢纽。在他的工作中，垂直港口的位置是基于机场旅客数据进行优化的。此外，地面基础设施的运营能力在提高 UAM 系统运营效率方面起着重要作用。参考文献[26]建议，在车辆规格保持不变的情况下，平衡登机口数量和车辆数量将最大化 UAM 模式的效用。

Figure 1. Examples of UAM landing sites. (a) UAM landing site design by repurposing underutilized parking structure rooftops [29]. (b) Lilium UAM vertiport in Lake Nona, Florida [30]. (c) Archetypes of UAM infrastructure [31].
图 1. UAM 降落点的示例。(a)通过重新利用未充分利用的停车场屋顶设计的 UAM 降落点[29]。(b)佛罗里达州 Lake Nona 的 Lilium UAM 垂直港口[30]。(c)UAM 基础设施的原型[31]。

3. Data and Analysis
数据和分析

3.1. Study Area 3.1. 研究区域

This study focuses on the Greater Los Angeles Area, which is composed of five populous counties (Los Angeles County, Orange County, San Bernardino County, Riverside County, and Ventura County). Supply-side infrastructure opportunities, such as heliports and elevated parking structures, are widely available to accommodate the regional deployment of UAM services (see Figure 2). Additionally, there is an increasing local interest in UAM adoption in the study area. Local government plays a vital role in gaining public trust for a new mode of transport, such as UAM, by facilitating conversations between community members and the private sector [32]. In December 2020, Mayor Garcetti announced a public–private partnership between the Los Angeles Department of Transportation and Hyundai Motor Group; this effort aims to introduce UAM to local airspace by 2023 [33]. One of the early goals of this partnership is to visualize a few vertiports where people can go flying on eVTOL. Moreover, the climate of southern California is classified as a Mediterranean climate, a type of dry subtropical climate. Such a climate is desirable for the all-year-round operation of UAM systems. Additionally, on the demand side, this region has seen a significant increase in ‘super commuters’ whose round trip to work is more than 3 h. According to the 2018 5-year American Community Survey, there are over 150,000 super commuters, or 1.5% of the total population in Los Angeles County [34].
该研究重点关注大洛杉矶地区，该地区由五个人口众多的县组成（洛杉矶县、橙县、圣贝纳迪诺县、河滨县和文图拉县）。供应端基础设施机会，如直升机停机场和高架停车场，广泛可用于容纳区域部署的城市空中交通服务（见图 2）。此外，该研究区域对城市空中交通的采用越来越感兴趣。地方政府在获得公众对城市空中交通等新型交通方式的信任方面发挥着重要作用，通过促进社区成员与私营部门之间的对话[32]。2020 年 12 月，加西亚市长宣布洛杉矶交通部与现代汽车集团之间的公私合作伙伴关系；该努力旨在到 2023 年将城市空中交通引入当地领空[33]。该合作伙伴关系的早期目标之一是可视化一些垂直起降机场，人们可以在电动垂直起降飞行器上飞行。此外，南加利福尼亚的气候属于地中海气候，是一种干旱亚热带气候。这种气候非常适合全年运营城市空中交通系统。 此外，在需求方面，该地区的“超级通勤者”数量显著增加，他们的上下班往返时间超过 3 小时。根据 2018 年的美国社区调查 5 年数据，洛杉矶县有超过 15 万名超级通勤者，占总人口的 1.5% [34]。

Figure 2. Study area.
图 2. 研究区域。

3.2. Data 3.2. 数据

This study collected two categories of data for the scenario analyses. On the demand side, this study relied on the U.S. Census Longitudinal Employer–Household Dynamics (LEHD) Origin–Destination Employment Statistics that capture home–workplace trips (origin/destination locations) and commuter characteristics at the census block level [12]. The LEHD dataset is based on employer reports of quarterly earnings that integrate workers and employers. Moreover, the LEHD data provided three categories of sociodemographic characteristics associated with commuting trips, including age groups, income levels, and occupations. The data breaks of different commuter characteristics are presented in Table 2 The scenario analysis used the LEHD Origin–Destination (OD) data to estimate the potential travel demand and commuter characteristics under different supply scenarios of vertiports. One of the advantages of LEHD data compared to other trip datasets, e.g., Census Transportation Planning Products (CTPP) sample-based surveys, is their comprehensive and timely OD geographical matrix for flows between households and workplaces at fine-grain that can be used in transportation analysis. However, LEHD does not include mode choices and travel costs information (i.e., costs, time, etc.). Therefore, this study focuses primarily on the accessibility of potential vertiport locations while the available data exclude the possibility of comparing the actual travel costs between UAM and other modes of travel.
该研究收集了两类数据用于情景分析。在需求方面，该研究依赖于美国人口普查局的纵向雇主-家庭动态（LEHD）起点-终点就业统计数据，该数据捕捉了家庭-工作地点之间的通勤行程（起点/终点位置）和通勤特征，以人口普查区块为单位[12]。LEHD 数据集基于雇主对季度收入的报告，整合了工人和雇主的信息。此外，LEHD 数据提供了与通勤行程相关的三类社会人口特征，包括年龄组、收入水平和职业。不同通勤特征的数据分段见表 2。情景分析使用 LEHD 起点-终点（OD）数据来估计在不同垂直起降机场供应情景下的潜在出行需求和通勤特征。与其他出行数据集（例如人口普查交通规划产品（CTPP）基于样本的调查）相比，LEHD 数据的一个优势是其全面且及时的起点-终点地理矩阵，可用于精细粒度的家庭和工作地点之间的流量交通分析。 然而，LEHD 不包括出行方式选择和出行成本信息（即成本、时间等）。因此，本研究主要关注潜在垂直起降机场位置的可达性，而可用数据不包括比较城市空中交通与其他出行方式的实际出行成本的可能性。

Table 2. Factors and data sources.

Categories	Factors	Data Sources	Unit of Observation	Year of Collection
Demand side	Age groups	Census Origin–Destination Employment Statistics	Census block	2019
	Occupation groups	Census Origin–Destination Employment Statistics	Census block	2019
	Income groups	Census Origin–Destination Employment Statistics	Census block	2019
	Home–workplace trips	Census Origin–Destination Employment Statistics	Census block	2019
	Home–workplace distance	Calculated from the centroids of census blocks	Census block	2019
Supply side	Helipads	The Federal Aviation Administration	Site	2019
	Parking structure	Google Earth satellite image/Google Map API	Site	2021
	UAS airspace and facility map	The Federal Aviation Administration	Zone	2020
	Schools	California School Campus Database	Land Parcel	2018
	Noise levels	The United States Department of Transportation	-	2018

On the supply side, this study considered data sources that captured landing site opportunities and spatial constraints. Infrastructure opportunities include helipads and elevated parking structures collected for the study area. The helipad location data was derived from the FAA’s National Airspace System Resource Aeronautical Data Product [35]. The helipad database is a geographic point database of aircraft landing facilities in the United States. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels, and usage categories. The elevated parking structure locations were piled with the Google Map API. Each elevated parking structure location was manually verified with Google Earth satellite images. Moreover, the supply-side constraints, including national airspace zones and local land use constraints (e.g., noise levels and schools) were collected for the analysis. Specifically, the noise data were retrieved from the National Transportation Noise Map, which allows the measurement of potential exposure to aviation and highway noise. No-fly-zone thresholds are defined in accordance with the UAS airspace regulation [36]. Table 2 summarizes the identified factors and corresponding data sources. The school zone datasets were collected from the California School Campus Database (CSCD) [37]. The CSCD dataset includes parcel-level campus boundaries of schools with kindergarten through K-12 instruction as well as colleges, universities, and community colleges. The collected datasets and their sources are presented in Table 2.
在供应方面，该研究考虑了捕捉到降落场机会和空间限制的数据来源。基础设施机会包括为研究区域收集的直升机停机坪和高架停车结构。直升机停机坪位置数据来源于 FAA 的国家空域系统资源航空数据产品[35]。直升机停机坪数据库是美国飞机着陆设施的地理点数据库。属性数据提供了着陆设施的物理和运营特征、当前使用情况（包括乘客人数和飞机操作）、拥堵水平和使用类别。高架停车结构位置使用了 Google 地图 API 进行堆叠。每个高架停车结构位置都通过 Google Earth 卫星图像进行了手动验证。此外，供应方面的限制，包括国家空域区域和当地土地利用限制（如噪音水平和学校），也被收集用于分析。具体而言，噪音数据来自国家交通噪音地图，该地图可以测量潜在的航空和公路噪音暴露。 无飞行区域的阈值根据无人机空域规定[36]进行定义。表 2 总结了确定的因素和相应的数据来源。学校区域数据集是从加利福尼亚学校校园数据库（CSCD）[37]收集的。CSCD 数据集包括幼儿园到 K-12 教育的学校以及大学和社区学院的校园边界。收集的数据集及其来源如表 2 所示。

3.3. Scenario Design 3.3. 场景设计

To understand how various spatial constraints affect the accessibility of potential vertiport locations among various commuting groups, this study applied different spatial constraint scenarios (see Table 3). This study defined five simple but crucial categories of rules to define demand-side constraints, supply-side constraints, and accessibility of vertiports. All these rules were mixed and applied to the five-county metropolitan area to measure the effects of spatial constraints on vertiports accessibility. The demand-coverage rules and block-distance rules enabled this study to further evaluate the impact of spatial constraints on vertiport location choice by home–work commuter characteristics (e.g., travel distance).
为了了解不同的空间约束如何影响不同通勤群体对潜在垂直起降场地的可达性，本研究应用了不同的空间约束情景（见表 3）。本研究定义了五个简单但关键的规则类别，以定义需求方约束、供给方约束和垂直起降场地的可达性。所有这些规则都被混合应用于五个县的都会区，以衡量空间约束对垂直起降场地可达性的影响。需求覆盖规则和区块距离规则使本研究能够进一步评估空间约束对垂直起降场地选择的影响，如居住地-工作地通勤者特征（例如，行程距离）。

Table 3. Summary of scenario analysis rules.

Categories	Rules	Description
Demand side	s000	Total number of home–workplace commuters
	sa01	Home–workplace commuters aged 29 or younger
	sa02	Home–workplace commuters aged 30 to 54
	sa03	Home–workplace commuters aged 55 or older
	se01	Home–workplace commuters with earnings of $1250/month or less
	se02	Home–workplace commuters with earnings of $1251/month to $3333/month
	se03	Home–workplace commuters with earnings greater than $3333/month
	si01	Home–workplace commuters in goods producing industry sectors
	si02	Home–workplace commuters in trade, transportation, and utility industry sectors
	si03	Home–workplace commuters in all other services industry sectors
Supply side	A1	≤200 ft preapproved UAS fly altitude
	A2	≤300 ft preapproved UAS fly altitude
	A3	≤400 ft preapproved UAS fly altitude
	S1	≥0.1 miles school buffer
	S2	≥0.25 miles school buffer
	S3	≥0.5 miles school buffer
	N1	≥85 dB noise level
	N2	≥70 dB noise level
	N3	≥60 dB noise level
Accessibility	D1	≤3 min walking by network distance
	D2	≤5 min walking by network distance
	D3	≤10 min walking by network distance
	D4	≤3 min driving by network distance
	D5	≤5 min driving by network distance
	D6	≤10 min driving by network distance
Demand coverage	J1	Home–workplace commuters w/only home block access
	J2	Home–workplace commuters w/only workplace block access
	J3	Home–workplace commuters w/both home and workplace access
Block distance	s000_10	Block centroid distance ≥10 miles
	s000_20	Block centroid distance ≥20 miles
	s000_30	Block centroid distance ≥30 miles

The demand-side rules considered home–workplace commuters by various population characteristics (e.g., age, income, and occupation) defined by the LEHD dataset (n2). Specifically, the rules included the total number of home–workplace commuters in each census block, age groups that divide the population by young (29 or younger), middle-aged (30 to 54), and senior (55 or older), income levels provided by LEHD that highlight income-disadvantaged populations (e.g., earnings of $1250/month or less), and occupation characteristics with a focus on blue-collar employers (e.g., commuters in trade, transportation, and utility industries). The available LEHD breakdowns of work–home commuters tend to highlight the economically disadvantaged population, which might create biases in understanding the impact of spatial constraints on middle-class commuters’ accessibility to vertiports.
根据 LEHD 数据集（n2）对家庭-工作地通勤者进行了各种人口特征（如年龄、收入和职业）的需求侧规则定义。具体规则包括每个人口普查区块中家庭-工作地通勤者的总数，将人口按年轻人（29 岁或以下）、中年人（30 至 54 岁）和老年人（55 岁或以上）分组，根据 LEHD 提供的收入水平来突出收入不利的人群（如每月收入 1250 美元或更少），以及职业特征，重点关注蓝领雇主（如贸易、运输和公用事业行业的通勤者）。LEHD 提供的工作-家庭通勤者的可用细分往往突出了经济弱势人口，这可能导致对中产阶级通勤者的空间限制对垂直港口可达性的影响的理解存在偏见。

To evaluate the effects of supply-side constraints on the accessibility to vertiports, this study proposes different sets of airspace, land use, and noise-level constraints. The airspace constraints use the FAA UAS Facility Map (UASFM), which depicts the maximum altitude in feet above ground level (AGL) that may be assigned by an FAA processor without additional internal FAA coordination. Although most commercial drones follow UASFM guidance in their ground control applications (e.g., no-fly zones and restrictive fly zones), it is unnecessarily applied to future UAM. Therefore, the use of the UASFM in this study serves as a relatively conservative rule when evaluating the impact of airspace constraints on vertiports, as UAM might share more airspace with manned aircrafts in the future than commercial drones. Apart from constraints in the airspace, existing noise levels on the ground also affect the vertiport choice. The noise data were recoded into three thresholds, 85 dB, 70 dB, and 60 dB, according to the noise impact classifications by the Centers for Disease Control and Prevention [38]. Noise at 60 dB is the equivalent sound level of everyday conversation. People may feel annoyed at 70 dB, and levels above 85 dB are damaging to hearing. Furthermore, this study defined accessibility rules by walking and driving time from the centroids of workplace/home census blocks. The access time was calculated with the ArcGIS online application by considering the speed limit and average traffic flows on the roads. Demand coverage and block distance rules were introduced to further evaluate the impact of supply-side constraints on commuters’ accessibility to vertiports by home–workplace travel distance. As shown in Figure 3, five categories of supply-side rules and demand-side rules are mixed to generate the statistics of scenario analyses.
为了评估供应侧约束对垂直港可达性的影响，本研究提出了不同的空域、土地利用和噪音水平约束。空域约束使用了 FAA 无人机设施地图（UASFM），该地图显示了 FAA 处理器可以在不需要额外的内部 FAA 协调的情况下分配的最大海拔高度（AGL）。尽管大多数商业无人机在地面控制应用中遵循 UASFM 的指导（例如禁飞区和限制飞行区），但未来的城市空中交通（UAM）不需要这样的约束。因此，本研究中使用 UASFM 作为评估空域约束对垂直港影响的相对保守规则，因为未来的 UAM 可能与载人飞机共享更多的空域。除了空域约束外，地面上现有的噪音水平也会影响垂直港的选择。根据疾病控制和预防中心的噪音影响分类，噪音数据被分为三个阈值，85 分贝、70 分贝和 60 分贝。60 分贝的噪音相当于日常对话的声音水平。 人们可能会对 70 dB 感到烦恼，而 85 dB 以上的声音对听力有损害。此外，该研究通过从工作场所/家庭普查区块的质心出发，定义了步行和驾车时间的可达性规则。通过考虑道路上的限速和平均交通流量，使用 ArcGIS 在线应用程序计算了访问时间。引入了需求覆盖和区块距离规则，进一步评估供应端约束对通勤者从家到工作场所的交通距离对垂直港口可达性的影响。如图 3 所示，混合使用五类供应端规则和需求端规则生成了情景分析的统计数据。

Figure 3. Scenario analysis framework.
图 3. 情景分析框架。

4. Results 4. 结果

The impact of spatial constraints on supply-side opportunities is presented in Appendix A. In general, current highway and airport noise have minimal impact on the location choice of vertiports. At the same time, airspace zoning and school buffer zones can significantly affect the location choice of vertiports. Even under the 200 ft flying altitude restriction, only 67.2% of available vertiports remain valid choices. If the aerial zoning designation keeps the current 400 ft flying altitude (for small drones) in place, only 57.8% of existing infrastructure remain valid, less than half of which are parking structures. By mixing the scenarios, this study has been able to generate 27 (three airspace constraints × three school buffer constraints × three noise level constraints) mixed scenarios. The summary statistics of mixed scenarios are presented in Appendix A. The best, the median, and the strictest mixed scenarios are presented in Table 4. Under the best mixed scenario, 61.9% of available sites remain valid, and slightly less than half are parking structures. The percentage of parking structures drops significantly when the constraints become stricter. Under the strictest mixed scenario, about 1/3 of valid sites are parking structures.
空间限制对供应侧机会的影响在附录 A 中呈现。总体而言，目前的公路和机场噪音对垂直起降场所选择的影响较小。与此同时，空域划分和学校缓冲区域可以显著影响垂直起降场所的选择。即使在 200 英尺的飞行高度限制下，只有 67.2%的可用垂直起降场所仍然是有效选择。如果空中划分指定保持当前的 400 英尺飞行高度（适用于小型无人机），只有 57.8%的现有基础设施仍然有效，其中不到一半是停车结构。通过混合不同情景，本研究能够生成 27 个（三个空域限制×三个学校缓冲限制×三个噪音水平限制）混合情景。混合情景的摘要统计数据在附录 A 中呈现。最佳、中位和最严格的混合情景在表 4 中呈现。在最佳混合情景下，61.9%的可用场地仍然有效，略少于一半是停车结构。当限制变得更严格时，停车结构的比例显著下降。 在最严格的混合情况下，大约有三分之一的有效场地是停车结构。

Table 4. Impact of spatial constraints on infrastructure opportunities (single-constraint scenarios and selected mixed-constraint scenarios).

Rules	Description	Helipads	Parking Structures	Total	% Base
No constraints	All available sites	360	444	804	100.0%
A1	≤200 ft airspace zoning	271	269	540	67.2%
A2	≤300 ft airspace zoning	260	242	502	62.4%
A3	≤400 ft airspace zoning	242	223	465	57.8%
S1	>0.1 mile school buffer	348	402	750	93.3%
S2	>0.25 mile school buffer	294	305	599	74.5%
S3	>0.5 mile school buffer	219	138	357	44.4%
N1	≥85 dB noise	360	444	804	100.0%
N2	≥70 dB noise	347	441	788	98.0%
N3	≥60 dB noise	325	418	743	92.4%
A1 × S1 × N1	The best mixed scenario	262	236	498	61.9%
A2 × S2 × N2	The median mixed scenario	217	148	365	45.4%
A3 × S3 × N3	The strictest mixed scenario	154	56	210	26.1%

Table 5 presents the home–workplace commuters who can access viable vertiports at their homes and workplaces under three different mixed scenarios. A breakdown of commuters with only home access or workplace access to vertiports is presented in Appendix A. The spatial distribution of valid sites under these mixed scenarios is shown in Figure 3. These results indicate that most vertiports are not within walkable distance from either the centroids of home census blocks or workplace census blocks in the study area. Under the best scenario, 15.8% of commuters can access the vertiports within 5 min driving distance while 70.7% of commuters can access the vertiports with less than 10 min of driving. This unique pattern is likely to make parking structures preferable to helipads when making location choices for vertiports. Please note that the calculation of driving distance has considered the speed limit of road networks but has not considered the live traffic and traffic light waiting time. Therefore, it is reasonable to believe that the results are optimistic estimates.
表 5 展示了在三种不同的混合情景下，能够在家和工作地点都能接近可行的垂直起降机场的通勤者。附录 A 中列出了只能在家或工作地点接近垂直起降机场的通勤者的细分情况。这些混合情景下有效站点的空间分布如图 3 所示。这些结果表明，在研究区域内，大多数垂直起降机场距离家庭普查区块的质心或工作地普查区块的质心都不在可步行距离内。在最佳情景下，15.8%的通勤者可以在 5 分钟的驾驶距离内接近垂直起降机场，而 70.7%的通勤者可以在不到 10 分钟的驾驶距离内接近垂直起降机场。这种独特的模式可能使停车设施在选择垂直起降机场的位置时更受青睐。请注意，驾驶距离的计算考虑了道路网络的限速，但没有考虑实时交通和红绿灯等待时间。因此，可以合理地认为这些结果是乐观估计。

Table 5. The accessibility of supply-side opportunities for home–workplace commuters (% population with both home and workplace access to vertiports).

Rules	Description	A1 × S1 × N1 (Best)	A2 × S2 × N2 (Median)	A3 × S3 × N3 (Strictest)
D1	≤3 min walking distance	0.13%	0.06%	0.00%
D2	≤5 min walking distance	0.37%	0.16%	0.01%
D3	≤10 min walking distance	1.50%	0.63%	0.08%
D4	≤3min driving distance	4.11%	1.88%	0.36%
D5	≤5 min driving distance	15.80%	8.30%	2.58%
D6	≤10 min driving distance	70.73%	49.48%	27.49%

The horizontal histograms under the maps in Figure 4a–c present the spatial coverage of different long-distance commuter groups with both home and workplace access to vertiports under three mixed scenarios. Compared to the population average coverage in Table 5, this study finds that commuter accessibility is not very sensitive to travel distance. In other words, long-distance commuters are likely to enjoy equal access to vertiports as average commuters. This piece of evidence may support the claim that current infrastructures are spatially ready to accommodate a regional network of urban air mobility. However, extreme-low-income and low-income populations have a systematically lower level of accessibility than the population average. Consequently, low-income populations are likely to lag in adopting the UAM commute mode. Blue-collar workers and young commuters also have lower-than-average levels of access to vertiports. Such patterns have remained mostly consistent across different accessibility constraints. Appendix A indicates that certain population groups are systematically disadvantaged in terms of their closeness to vertiports regardless of travel distance or accessibility definitions.
图 4a-c 中地图下方的水平柱状图显示了在三种混合情景下，具有家庭和工作场所接入垂直港口的不同长途通勤群体的空间覆盖范围。与表 5 中的人口平均覆盖率相比，本研究发现通勤可达性对出行距离不太敏感。换句话说，长途通勤者很可能与普通通勤者一样能够平等地接入垂直港口。这一证据可能支持当前基础设施已经具备空间条件来容纳区域性城市空中移动网络的说法。然而，极低收入和低收入人口的可达性水平普遍低于人口平均水平。因此，低收入人口可能在采用 UAM 通勤模式方面落后。蓝领工人和年轻通勤者对垂直港口的接入水平也低于平均水平。这种模式在不同的可达性限制下基本保持一致。 附录 A 表明，无论旅行距离或可达性定义如何，某些人群在靠近垂直起降机场方面存在系统性的劣势。

Figure 4. (a) Spatial distribution of supply-side opportunities and demand-side coverage by user groups based on the best scenario. (b) Spatial distribution of supply-side opportunities and demand-side coverage by user groups based on the median scenario. (c) Spatial distribution of supply-side opportunities and demand-side coverage by user groups based on the strictest scenario.
图 4. (a) 基于最佳情景的供应机会和用户群体需求覆盖的空间分布。(b) 基于中位情景的供应机会和用户群体需求覆盖的空间分布。(c) 基于最严格情景的供应机会和用户群体需求覆盖的空间分布。

Finally, this study has identified the top home-based and workplace-based vertiport candidates based on eligible parking structures for long-distance commuters in the study area from the above analysis (Figure 5). The top home-based vertiports are located in suburban areas. In contrast, the workplace-based vertiports are situated in popular job centers due to the jobs–housing mismatch in southern California. These potential vertiport candidates represent different strategies that future UAM vertiports might take. For instance, suburban districts have been identified with many similar locations, such as elevated parking structures next to a large shopping center, to be adaptively redesigned as vertiports. Station C represents the opportunities to encourage multimodal travel by incorporating UAM into public transport hubs. Station F demonstrates the opportunities of the UAM travel mode to serve both regular commuters and be part of medical emergency services. Moreover, these vertiports may serve as magnets for planners to revitalize urban places by bringing people, activities, and public places together near UAM hubs (e.g., vertiport-oriented communities).
最后，根据上述分析，本研究确定了基于合格停车设施的长途通勤者在研究区域内的家庭和工作场所垂直港候选地点（图 5）。顶级家庭垂直港位于郊区地区。相反，基于工作场所的垂直港位于南加州热门就业中心，这是由于就业与住房不匹配的原因。这些潜在的垂直港候选地代表了未来城市空中交通垂直港可能采取的不同策略。例如，郊区地区已被确定为许多类似位置的地方，例如位于大型购物中心旁边的高架停车设施，可以进行适应性重新设计成垂直港。C 站代表了通过将城市空中交通纳入公共交通枢纽来鼓励多式联运的机会。F 站展示了城市空中交通出行方式为常规通勤者提供服务并成为医疗急救服务的机会。此外，这些垂直港还可以作为吸引规划者通过将人们、活动和公共场所聚集在城市空中交通枢纽附近来振兴城市地区的磁铁（例如，以垂直港为导向的社区）。

Figure 5. A proposed UAM network based on the top home-based and workplace-based stations for residents having commuting trips over 10 miles and living within 10 min driving distance of vertiports.
图 5. 基于家庭和工作场所为基础的顶级站点，为居民提供超过 10 英里的通勤行程，并且居住在离垂直起降场不超过 10 分钟车程的地方，提出了一种城市空中交通网络。

5. Conclusions and Discussion
5. 结论和讨论

Urban air mobility holds the promise of becoming a greener, faster, and quieter mode of aerial transportation in the near future [4]. While the adoption of UAM faces many social acceptance barriers [19,38], identifying feasible landing site locations in built-out metropolitan areas remains a major physical barrier to the mass deployment of UAM. This study provides an initial assessment of potential vertiports locations and associated travel demand in southern California by employing a systematic scenario analysis. This study suggests that even under the best scenario, most vertiport locations are not within walkable distance for home–workplace commuters. This pattern is rooted in the urban development patterns of the study area but will make parks and rides a necessary strategy to access UAM in the future. As a result, parking structures, which are already equipped with parking capacity, become a preferable choice of infrastructure to accommodate the future deployment of UAM services. This study also suggests that extremely low-income populations and blue-collar workers have lower accessibility to vertiports regardless of travel distance. Therefore, future policy interventions might be needed for equitable access to this new mode of transportation. This study also extends the analysis by proposing a network of UAM stations in the study area. The illustration provides possible strategies for planners to identify feasible UAM stations that can facilitate the mass adoption of UAM.
城市空中移动在不久的将来有望成为一种更环保、更快速、更安静的空中交通方式。尽管城市空中移动的采用面临着许多社会接受障碍，但在已建成的大都市地区确定可行的降落场地位置仍然是大规模部署城市空中移动的主要物理障碍。本研究通过采用系统的情景分析，对南加利福尼亚的潜在垂直港口位置和相关出行需求进行了初步评估。研究表明，即使在最佳情景下，大多数垂直港口位置对于居住地-工作地通勤者来说并不在可步行距离内。这种模式根植于研究区域的城市发展模式，但将使停车场和乘车点成为未来访问城市空中移动的必要策略。因此，已经配备停车位的停车结构成为容纳未来城市空中移动服务部署的首选基础设施。本研究还指出，极低收入人群和蓝领工人无论旅行距离如何，对垂直港口的可达性较低。 因此，为了实现对这种新型交通方式的公平访问，未来可能需要政策干预。本研究还通过提出一个 UAM 站点网络来扩展分析。这个示意图提供了可能的策略，帮助规划者确定可行的 UAM 站点，以促进 UAM 的大规模采用。

Admittedly, this study has several limitations. For instance, supply-side opportunities are not equal to supply-side capacity. The present study does not delve into the utility aspects (e.g., timesaving and costs) of the location choices of UAM vertiports but primarily focuses on supply-side constraints, such as noise levels, school zones, and no-fly zones, and how various user groups might have disproportional access to available vertiports. As briefly discussed in the conclusion section, a major challenge to studying the utility aspects of UAM vertiports placement is the lack of UAM operation data (e.g., ingress/egress time, charging cycles, scheduling, aircraft specification, etc.) [39]. Furthermore, to address the limitation of existing studies, future research might explore the integration of UAM and its impact on cities in the following three major research trajectories: (1) conducting utility-based studies by integrating UAM operation data; (2) conducting multimodal transportation modeling by including UAM operation specifications and vertiports network design; and (3) integrating user adoption factors (e.g., elasticity between user characteristics and UAM demand) in UAM transportation modeling.
诚然，这项研究有几个限制。例如，供给侧机会并不等同于供给侧能力。本研究没有深入探讨 UAM 垂直起降机场选址的效用方面（例如节省时间和成本），而主要关注供给侧的限制，如噪音水平、学区和禁飞区，以及各种用户群体可能对可用垂直起降机场的不成比例的访问。正如在结论部分简要讨论的那样，研究 UAM 垂直起降机场选址的效用方面的一个主要挑战是缺乏 UAM 运营数据（例如进出时间、充电周期、调度、飞机规格等）[39]。此外，为了解决现有研究的局限性，未来的研究可以探索以下三个主要研究方向：（1）通过整合 UAM 运营数据进行效用研究；（2）通过包括 UAM 运营规格和垂直起降机场网络设计进行多模式交通建模；（3）整合用户采用因素（例如在 UAM 交通建模中考虑用户特征和 UAM 需求之间的弹性。

Unlike traditional aviation, which by design typically spends the majority of flight time over sparsely populated areas, UAM operations will generally occur over metropolitan areas that are densely populated in terms of people and property. As such, UAM concepts, technologies, and procedures must be designed and managed with safety in mind from the start [40]. As with any new entrant to the airspace, UAM aircraft and operations should be designed in a way to earn acceptance by the public [41]. Additionally, several barriers must be overcome beyond locational constraints for UAM operations to be integrated safely and efficiently into the urban airspace system. The obstacles that are more closely associated with UAM vehicles include ride quality, lifecycle emissions, ease of certification in terms of both time and cost, visual and noise nuisance perceived by the community on the ground, affordability in terms of operating cost, safety in terms of casualties and property damage, and efficiency in terms of energy usage. Concerns about potential privacy violations, auditory and visual disturbances, safety risks, and affordability are some of the significant factors that should be carefully investigated.
与传统航空不同，传统航空通常在人口稀少的地区飞行时间占大部分，而城市空中交通（UAM）的运营通常发生在人口密集的都市地区。因此，UAM 的概念、技术和程序必须从一开始就考虑安全性[40]。与任何新进入空域的飞行器一样，UAM 飞行器和运营应该设计成能够赢得公众的接受[41]。此外，除了位置限制之外，还必须克服一些障碍，以便将 UAM 运营安全高效地整合到城市空域系统中。与 UAM 飞行器更密切相关的障碍包括乘坐质量、生命周期排放、认证的便利性（包括时间和成本）、地面社区对视觉和噪音的困扰、运营成本的可负担性、伤亡和财产损失的安全性，以及能源使用的效率。 对潜在的隐私侵犯、听觉和视觉干扰、安全风险和经济承受能力的担忧是一些需要仔细调查的重要因素。