Urban Air Mobility Vertiports: A Bibliometric Analysis of Applications, Challenges, and Emerging Directions
Abstract
1. Introduction
2. Vertiport Basics and Industry Status
2.1. Mechanisms of UAM
2.2. Definition of Vertiport
2.3. Current State of Vertiport Development
3. Data Sources and Methods
3.1. Data Collection
- Web of Science Core Collection (WoSCC): 571 records
- Scopus (Elsevier): 201 records
- IEEE Xplore: 301 records
- AIAA (American Institute of Aeronautics and Astronautics): 112 records
3.2. Methods
- Identify influential contributors, focusing on leading countries and institutions through keyword and bibliometric analyses.
- Explore recent and emerging research frontiers in vertiport.
4. Results of the Knowledge Mapping Analysis
4.1. Basic Situation Analysis
4.1.1. Trends in the Number of Published Papers
- Initial Stage (2000–2015):
- Drawing on ICAO Annex 14 (Heliports) as the regulatory foundation;
- Conceptual definitions of vertiports, often used interchangeably with terms such as VTOL ports;
- Preliminary considerations of air taxi systems remain largely speculative.
- 2.
- Rapid Development Stage (2016–2024):
- Technological breakthroughs in eVTOL aircraft have created an urgent need for supporting ground infrastructure.
- Industry momentum: Major companies such as Volocopter, Joby Aviation, Lilium, and EHang initiated large-scale demonstration projects, requiring dedicated ground infrastructure.
- Regulatory progress: The FAA, NASA, and EASA issued the Prototype Technical Design Specifications for Vertiports, providing the first structured regulatory frameworks.
- Diversification of research themes, expanding from basic siting and design issues to capacity management, charging and energy infrastructure, multimodal integration, and safety considerations.
4.1.2. Distribution of Country/Region
4.1.3. Distribution of Research Institutions
4.1.4. Document Co-Citation Analysis
4.2. Keyword Analysis
5. Research Themes
- Vertiport Design: work related to infrastructure configuration, design standards, and layout planning.
- Capacity: research addressing vertiport capacity, operational flow, and scheduling efficiency.
- Location optimization: studies focusing on site selection methods, accessibility, and network efficiency.
- Charging station location–allocation problem: research addressing optimal placement, sizing, and integration with grid constraints.
- UAM operations management: studies on fleet assignment, scheduling, and service performance.
5.1. Theme 1: Vertiport Design
- Touchdown and Lift-Off Area (TLOF): The core surface on which aircraft take off and land, designed to be obstruction-free and constructed with materials suitable for eVTOL operations.
- Final Approach and Take-Off Area (FATO): A safety-buffered perimeter surrounding the TLOF that ensures safe aircraft ingress and egress.
- Safety Area: The buffer zone surrounding the FATO is designed to ensure that aircraft deviating from their intended takeoff or landing paths do not collide with obstacles.
- Parking and Landing Areas: For VTOL aircraft waiting between operations, undergoing pre-flight checks, or charging.
- Passenger Terminal: Check-in desks, security screening zones, waiting lounges, restrooms, and boarding gates.
- Multimodal Transport Hubs: Integration with public transport, ride-sharing zones, taxi bays, and bicycle parking areas.
- Computational Fluid Dynamics (CFD): Used to analyze rotor downwash effects and airflow interference to improve the aerodynamic safety and efficiency of TLOF and FATO areas [96].
- Queuing Theory Models: Applied to estimate vertiport throughput and optimize the number of TLOF/FATO pairs under stochastic demand [97].
- Discrete-Event Simulation (DES): Enables evaluation of vertiport performance under different demand scenarios and layout configurations [98].
- Optimization Algorithms: Mixed-integer linear programming (MILP) and heuristic methods are used for layout configuration, pad allocation, and gate assignment [13]
- GIS-Based Spatial Analysis: Supports the assessment of vertiport location feasibility and integration with existing urban infrastructure [99].
5.2. Theme 2: Capacity Planning
- Stands Capacity
- Take-off and Landing Capacity
- System-level throughput and bottleneck identification
5.3. Theme 3: Location Optimization
- Multi-Criteria Decision-Making (MCDM)
- Facility location optimization models
- Network analysis and graph-theoretical modeling
5.4. Theme 4: Charging Station Location–Allocation Problem
5.5. Theme 5: UAM Operations Management
6. Conclusions
- From Static to Dynamic Models—moving beyond static siting and sizing toward dynamic, data-driven frameworks capable of multi-dimensional optimization.
- Capacity and Robustness—strengthening capacity management while incorporating robust and scenario-based methods to address demand and operational uncertainties.
- Digital Twin Platforms—developing integrated simulation environments for real-time prediction, multi-scenario testing, and balancing efficiency with community acceptance.
- Cross-Disciplinary and Comparative Studies—combining insights from transportation, urban planning, energy, and AI, and expanding to multi-city studies to ensure generalizability.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Term | Definition |
---|---|---|
Key Concepts | Urban Air Mobility (UAM) | The safe and efficient air traffic operations in a metropolitan area for manned aircraft and unmanned aircraft systems. |
Advanced Air Mobility (AAM) | A system of safe, affordable, and automated air transportation for passengers and cargo in urban and rural settings. | |
Aerial Vehicle | Vertical Take-Off and Land (VTOL) | An aircraft that can take off, hover, and land vertically. |
electric vertical take-off and landing (eVTOL) | A variety of VTOL aircraft use electric power to hover, take off, and land vertically. | |
Unmanned Aerial Vehicles (UAV) | Multi-use aircraft with no human pilot aboard, commonly referred to as ‘drones’. | |
Air Traffic Management | Unmanned Aircraft Systems (UAS) | An aircraft system that operates without a human pilot on board and is either remotely controlled by human operators or operates autonomously based on pre-programmed instructions or sensor inputs. |
Unmanned Aircraft System Traffic Management (UTM) | A traffic management system that provides airspace integration requirements, enabling safe low-altitude operations of unmanned aircraft. | |
City Infrastructures | Vertiport | Ground facility designed to support the operations of UAM aircraft, providing essential services. |
Communication, Navigation, and Surveillance (CNS) infrastructure | Communication, Navigation, and Surveillance systems enabling secure aircraft-to-infrastructure and aircraft-to-aircraft interaction. | |
Urban Mobility Hub | A multimodal transfer center where vertiports are connected to existing ground transport (e.g., metro, bus, taxi, shared mobility). |
Term | Scale | Description | Reference |
---|---|---|---|
Pocket Airpark | Compact | Small-scale airfield concepts adapted to dense urban areas. | Seeley et al. [27] |
Vertistop | small | Proposed as minimal-infrastructure stopover points within distributed UAM networks. | Salehi et al. [28] |
Vertipad | small | Compact landing pad for single eVTOL operations; often proposed in early infrastructure feasibility studies. | Johnston et al. [29] |
Skynode | small | A very small UAM landing site, typically with a single pad and minimal facilities, serves as a local access node in the network. | Schweiger et al. [12] |
Skypark | Medium | UAM facility with multiple pads and basic passenger or cargo services, functioning as a community-level hub. | Schweiger et al. [12] |
Skyport | small to large | Marketing-driven term proposed in industry white papers for eVTOL passenger hubs. | Schweiger et al. [12] |
Vertiport | Medium to large | Ground facility designed to support the operations of UAM aircraft, providing essential services. | Holden et al. [21,30] |
Vertihub | Large | A centralized facility handling multiple aircraft with boarding, charging, and logistics functions. | Lineberger et al. [31] |
Vertidrome | Large | Envisioned as a hybrid between a vertiport and a droneport for mixed traffic. | Schweiger et al. [32] |
Country/Region | Research Focus | Representative Projects |
---|---|---|
United States | UAM ConOps [33], Engineering Brief No. 105: Vertiport Design [25,55] | City-level UAM pilot projects in cities like Los Angeles, Miami |
Europe | Prototype Technical Design Specifications for Vertiports [41], SESAR Joint Undertaking Reports [56] | Demonstration projects in Paris, Munich, and Rome |
China | Technical Requirements for eVTOL Landing Fields [57] | UAM hubs in Shenzhen, Hefei, and other cities |
South Korea | K-UAM Grand Challenge [58,59] | UAM demonstrations in Seoul and Jeju Island |
Middle East | Dubai UAM Strategy [60], NEOM Transportation Strategy [61] | Vertiport plans in Dubai and NEOM city transportation in Saudi Arabia |
Research Team/Institution | Research Focus Core Research Focus | Ref |
---|---|---|
NASA Langley Research Center | Airspace Management and Operational Integration, Safety Standards, Regulatory Frameworks | [35,62,63] |
UC Berkeley | Urban Transportation and UAM Network Planning, Social Equity Analysis | [64,65,66] |
Georgia Institute of Technology | Multimodal Transportation and UAM Integration, Path Optimization, Operational Scheduling | [67,68,69] |
TU Munich | Vertiport Design and Capacity Simulation, Topology Analysis and Optimization | [67,70,71] |
Korea Aerospace University | Integration of UAM with Smart Cities, Environmental Impact, and Noise Analysis | [72,73] |
Tsinghua University | Low-altitude Economy and Policy-Driven Research, Multi-objective Site Optimization | [74,75] |
Beijing Jiaotong University | Transportation Accessibility Analysis, Multimodal Transportation Systems, Capacity Planning | [76,77] |
Massachusetts Institute of Technology | Flight Path Optimization, Airspace Design and Integration | [78,79] |
Hong Kong Polytechnic University | Traffic Simulation and Demand Modeling, Aircraft Scheduling and Service Optimization | [80,81] |
NO | Keyword | Frequency | NO | Keyword | Frequency |
---|---|---|---|---|---|
1 | Vertiport Design | 62 | 21 | Air Traffic Control | 7 |
2 | Airspace Management | 50 | 22 | Infrastructure Resilience | 4 |
3 | Capacity Optimization | 48 | 23 | Urban Air Traffic | 4 |
4 | Smart Airspace Integration | 44 | 24 | Noise Reduction | 3 |
5 | Noise Impact | 40 | 25 | Public Policy | 3 |
6 | Charging Infrastructure | 38 | 26 | Vertiport Capacity Analysis | 3 |
7 | Multimodal Transportation | 35 | 27 | Logistics Integration | 3 |
8 | Environmental Impact | 32 | 28 | Environmental Sustainability | 3 |
9 | Queueing Theory | 30 | 29 | Digital Twin Technology | 3 |
10 | Site Selection Optimization | 19 | 30 | Charging Time | 3 |
11 | Operational Efficiency | 18 | 31 | Flight Time | 3 |
12 | Vertiport Sizing | 17 | 32 | Digital Infrastructure | 2 |
13 | Infrastructure Integration | 16 | 33 | Passenger Flow | 2 |
14 | Autonomous Operation | 16 | 34 | Real-Time Data | 2 |
15 | Safety Standards | 15 | 35 | Traffic Modeling | 2 |
16 | Flight Path Optimization | 14 | 36 | Regulatory Compliance | 2 |
17 | Connectivity | 11 | 37 | Urban Planning | 2 |
18 | Urban Mobility Integration | 11 | 38 | Vertiport Simulation | 2 |
19 | Vertiport Accessibility | 8 | 39 | High-Altitude Operations | 2 |
20 | Charging Stations | 8 | 40 | Commercialization | 2 |
Reference | TLOF | FATO | Safety Area (SA) |
---|---|---|---|
Seeley (2017b) [27] | — | — | 550 × 325 ft |
Uber Elevate (2016) [21,86] | 50 × 50 ft | 75 × 75 ft | 125 ft |
Alexander and Syms (2017) [87] | 45 × 45 ft | 70 × 70 ft | 100 ft |
Vascik et al. (2017) [88] | 50 × 50 ft | — | — |
Syed et al. (2017) [89] | 43 × 43 ft | 65 E 65 ft | 95 ft |
Antcliff et al. (2016) [90] | 50 × 50 ft | 100 × 100 ft | 125 ft |
EASA [41] | — | — | 2D × 2D |
FAA [25] | 1D × 1D | 1.5D × 1.5D | 3D ×3D |
ICAO [42] | 0.83D | 1D | 1.25D |
Method | Advantage | Limitations |
---|---|---|
MAS [94,95] | Captures complex interactions among eVTOLs, ground vehicles, and passengers; suitable for dynamic demand and congestion analysis; reveals emergent system behaviors. | Requires detailed behavioral and calibration data; computationally intensive; results may be sensitive and lack analytical tractability. |
CFD [96] | Provides accurate analysis of rotor downwash, airflow interference, and wind fields; directly informs TLOF/FATO aerodynamic safety and design. | High computational cost; limited to small-scale or localized simulations; difficult to scale to network-level planning. |
Queuing Theory Models [97] | Mathematically tractable and efficient; estimates throughput, waiting times, and utilization quickly; suitable for early-stage capacity assessment. | Relies on simplified assumptions; limited in capturing operational complexity; results may deviate from reality. |
DES [98] | Flexibly models detailed operational processes; evaluates performance under different demand scenarios and layouts. | Requires extensive parameterization; lacks general closed-form solutions; simulation runs may be costly for large scenario sets. |
MILP [13] | Formulates rigorous models for layout, pad allocation, and scheduling; MILP offers optimal or near-optimal solutions; heuristics handle large-scale problems efficiently. | MILP becomes intractable for large networks; heuristic solutions may lack guaranteed optimality and reproducibility. |
GIS-Based Spatial Analysis [99] | Visually demonstrates site feasibility and integration with urban infrastructure; allows multi-criteria overlay. | Primarily static analysis without a dynamic operational perspective; results depend heavily on data accuracy; limited in handling time-dependent constraints. |
Layout Type | Characteristics | Advantages | Limitations |
---|---|---|---|
Linear Layout [13] | TLOF/FATO and stands arranged in a straight line; suitable for narrow sites or runway-adjacent areas. | Compact footprint; simple design and construction; easy management. | Low operational flexibility; prone to serial queuing bottlenecks; limited separation from downwash interference. |
Parallel Layout [101] | Multiple runways/pads aligned in parallel rows. | Enables higher simultaneous throughput, suits medium-demand hubs, and allows more orderly operations. | Requires a larger land area; stricter safety and wake separation management. |
Radial/Centralized Layout [13] | Pads and stands arranged around a central terminal or core. | Short passenger walking distances; efficient transfers; higher density of pads in limited space. | Complex downwash interactions; congestion risk in central areas; requires advanced scheduling. |
Distributed Layout [13] | Several smaller vertiports dispersed across a region instead of one large hub. | Expands service coverage, enhances flexibility and redundancy, and reduces congestion at central hubs. | Higher investment and operating costs; more complex coordination and management. |
Integrated Multimodal Layout [13] | Designed in conjunction with ground transport hubs (metro, bus, rail). | Strengthens intermodal connectivity; improves passenger experience; enables high passenger throughput. | High initial construction cost; requires long-term cross-agency planning and coordination. |
Method | Characteristics | Advantages | Limitations |
---|---|---|---|
AHP [109] | Decomposes complex decision problems into hierarchical structures; relies on pairwise comparisons. | Simple and intuitive; widely used in site selection; incorporates expert judgment systematically. | Relies on subjective input; consistency issues may arise in large-scale problems. |
TOPSIS [110,111] | Ranks alternatives based on distance from an ideal and a negative-ideal solution. | Clear ranking; computationally simple; effective for quantitative evaluation. | Sensitive to normalization and weight assignment; does not account for correlations among criteria. |
Fuzzy AHP [112] | Extends AHP by incorporating fuzzy set theory to handle vagueness in expert judgments. | Better handles uncertainty; more robust than traditional AHP. | More complex to implement; requires advanced knowledge of fuzzy set theory. |
VIKOR [113] | Focuses on ranking and selecting alternatives based on multi-criteria compromise solutions. | Balances conflicting objectives; suitable under uncertainty. | Results highly dependent on weights; may lack transparency for non-experts. |
Model Type | Characteristics | Advantages | Limitations |
---|---|---|---|
MCLP (Maximal Covering Location Problem) | Maximize the number of demand points covered within a fixed service radius and a limited number of facilities. | Intuitive and computationally efficient; suitable for uneven demand distribution. | Ignores service quality differences; limited resources may leave some demand points uncovered. |
LSCP (Location Set Covering Problem | Ensure all demand points are covered with the minimum number of sites. | Guarantees full coverage with fewer facilities. | May result in excessive facilities; lacks efficiency and balance considerations. |
P-Median Model | Minimizes the weighted average distance between demand points and facilities for a fixed number of p. | Balances efficiency and fairness; accounts for demand distribution. | May neglect fairness for remote demand points; computationally intensive for large problems. |
PCM (Progressive Coverage Model | Classifies demand into full, partial, and unmet coverage, assigning different weights to reflect realistic service levels. | Reflects realistic service levels; adaptable to uncertainty and limited resources; considers fairness. | Modeling is more complex; requires defined service tiers and weights; less transparent. |
Hybrid Models | Simultaneously optimize for service coverage and system efficiency | Integrates coverage, efficiency, cost, and equity; flexible for multi-objective, large-scale problems. | Higher modeling and computational complexity; sensitive to parameters; less transparent. |
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Lu, Y.; Zeng, W.; Wei, W.; Wu, W.; Jiang, H. Urban Air Mobility Vertiports: A Bibliometric Analysis of Applications, Challenges, and Emerging Directions. Appl. Sci. 2025, 15, 10961. https://doi.org/10.3390/app152010961
Lu Y, Zeng W, Wei W, Wu W, Jiang H. Urban Air Mobility Vertiports: A Bibliometric Analysis of Applications, Challenges, and Emerging Directions. Applied Sciences. 2025; 15(20):10961. https://doi.org/10.3390/app152010961
Chicago/Turabian StyleLu, Yannan, Weili Zeng, Wenbin Wei, Weiwei Wu, and Hao Jiang. 2025. "Urban Air Mobility Vertiports: A Bibliometric Analysis of Applications, Challenges, and Emerging Directions" Applied Sciences 15, no. 20: 10961. https://doi.org/10.3390/app152010961
APA StyleLu, Y., Zeng, W., Wei, W., Wu, W., & Jiang, H. (2025). Urban Air Mobility Vertiports: A Bibliometric Analysis of Applications, Challenges, and Emerging Directions. Applied Sciences, 15(20), 10961. https://doi.org/10.3390/app152010961