Urban air mobility (UAM) combines urban passenger and cargo air services, while regional air mobility (RAM) covers longer regional journeys and advanced air mobility (AAM) is the broader umbrella. The figures below distinguish surveys from modeled estimates and forecasts, and identify the population, geography and measurement period for each result.
Key Urban Air Mobility Statistics
The most useful headline figures span attitudes, demand, willingness to pay and operating scale:
- In an early 2021 survey of European Union citizens, 83% expressed a very or rather positive initial attitude toward UAM.
- In the same early 2021 EU survey, 71% said they were likely to use at least one UAM service.
- In early 2021, 43% of surveyed EU citizens were interested in both an air taxi and an air metro service.
- In the early 2021 EU survey, 71% expected UAM to improve emergency response time.
- In the early 2021 EU survey, 51% expected UAM to reduce traffic jams.
- In the early 2021 EU survey, 48% expected UAM to reduce local emissions.
- In the UK 2025 scenario model, UAM take-up coefficients ranged from 0.12% to 0.84% of initial taxi distance substituted.
- NASA’s model projected 121 million people in the 15 largest US cities in 2030, compared with 108 million in 2016.
- NASA’s 2017–2030 US demand model assumed 3% annual growth for commuter demand.
- In Milan, a 2021–2022 survey estimated €48–€69 per hour as willingness to pay for airport-shuttle UAM time savings.
- In Milan, city-taxi UAM willingness to pay for time savings was €34–€44 per hour in a 2021–2022 survey.
- A South Korean airport-shuttle study estimated mean willingness to pay of 60,173 KRW per 30-minute UAM trip.
- A German stated-preference study modeled up to 31% mode share for 19-seat commuter services among business travelers.
- NASA’s 2030 air-metro scenario used 2–5 passenger vehicles with an average load of 3 passengers per trip.
- NASA’s 2030 high-impact infrastructure range was 2,500–3,500 vertiports in its US modeled metro network.
- NASA’s 2030 model output was 35,000–40,000 air-metro vehicles in the US scenario.
- NASA’s 2030 model output was 20,000–25,000 air-taxi vehicles in the US scenario.
- NASA’s 2030 last-mile delivery scenario projected 0.5 billion deliveries in the US adjacent UAS delivery case.
- NASA estimated $30 million–$50 million for central air-traffic-management infrastructure in its 2030 US scenario.
Contents
- Public acceptance and expected benefits
- Demand, trip patterns and market forecasts
- Willingness to pay and travel-time value
- Aircraft, vertiports and operating scale
- Delivery, infrastructure and cost benchmarks
- Safety, noise, emissions and adoption constraints
Urban Air Mobility Public Acceptance and Expected Benefits
The European Union results come from EASA’s early 2021 survey context. They measure stated attitudes and expectations, not observed ridership or measured operational outcomes.
- 71% of surveyed EU citizens said they were likely to use at least one UAM service, while 43% were interested in using both an air taxi and an air metro service.
- 71% expected UAM to improve emergency response time, 51% expected fewer traffic jams, 48% expected lower local emissions, and 41% expected UAM to help develop remote areas.
- For air taxis, environmental and noise concerns each reached 38%, safety reached 37%, and security reached 29% of respondents selecting each concern.
- For drones, safety was the leading listed concern at 44%, followed by security at 39%, environmental issues at 35%, and noise at 28%.
The drone concern figures are adjacent to, but not identical with, passenger-UAM concerns. EASA described first UAM operations as expected within 3–5 years of its 2021 survey page publication, which was a contemporaneous expectation rather than an observed launch date. EASA, “Urban air mobility – 10 key survey results”
Urban Air Mobility Demand, Trip Patterns and Market Forecasts
Demand models illustrate how sensitive UAM forecasts are to definitions and adoption inputs. A UK government scenario model defines UAM as replacing taxi journeys of 5–100 km, while RAM covers taxi journeys over 100 km; these are scenario definitions, not universal industry boundaries.
| Model or input | Value | Period and geography |
|---|---|---|
| UK UAM take-up coefficients | 0.12%, 0.25%, 0.50%, 0.84% | Entry-into-service scenarios, UK |
| UK willingness-to-use inputs | 1%, 1.59%, 2.27%, 3% | Entry-into-service scenarios, UK |
| NASA modeled population | 108 million to 121 million | 2016 to 2030, 15 largest US cities |
| NASA commuter growth assumption | 3% annually | 2017–2030, US model |
| NASA non-commute growth assumption | 1% annually | 2017–2030, US model |
The UK model based initial UAM demand on taxi journeys between 5 and 100 km and excluded 2020 and 2021 from its 2002–2022 taxi-distance baseline because of COVID-19 impacts. Its take-up range remains below 1% in every entry-into-service scenario, so it should not be read as a measured national adoption rate. UK Department for Transport, “Future of Flight Scenarios Futures Research Report”
NASA’s US model assumed 18% of commuters were part-time and 82% full-time, with part-time commuters making 4 trips per week, full-time commuters making 10, and commuters traveling 45 weeks per year. These are model assumptions used to calculate addressable demand, not observed UAM trips. NASA, “Urban Air Mobility: Concepts and Challenges”
Air-Taxi Willingness to Pay and Travel-Time Value
Stated-preference studies show that the value of UAM depends heavily on trip purpose, airport access and traveler type. The figures below are willingness-to-pay estimates or modeled probabilities, not current fares or realized revenue.
| Market and measure | Result | Measurement context |
|---|---|---|
| Milan airport-shuttle time savings | €48–€69/hour | 2021–2022 survey, Milan metropolitan area |
| Milan city-taxi time savings | €34–€44/hour | 2021–2022 survey, Milan metropolitan area |
| South Korean airport shuttle mean WTP | 60,173 KRW/30 minutes | Incheon airport access study |
| South Korean airport shuttle median WTP | 48,521 KRW/30 minutes | Incheon airport access study |
| German hybrid-electric commuter WTP | €102–€130/hour | German business travelers, regional service |
In Milan, airport UAM willingness to pay was reported as 44%–57% greater than willingness to pay for within-metropolitan UAM travel. Business travelers’ willingness to pay was 31%–44% higher than that of travelers with other purposes in the same survey-model comparison. Transport Policy, “Urban Air Mobility (UAM): Airport shuttles or city-taxis?”
In the South Korean study, taxi users had a 97% higher modeled probability of willingness to pay for the UAM airport shuttle than the reference group, private-car drivers had a 34% higher modeled probability, and respondents aware of UAM technology had a 19% higher modeled probability. These are regression associations, not market-share estimates. Research in Transportation Business & Management, “Understanding determinants of willingness to pay for airport shuttle service of urban air mobility”
The German study modeled up to 31% mode share for 19-seat commuter services among business travelers. Its €102–€130 per hour time-savings figure concerns regional, rather than strictly urban, mobility and therefore belongs to the related RAM evidence base. Journal of Air Transport Management, “User preferences for Regional Air Mobility: Insights from Germany”
UAM Aircraft, Vertiports and Operating-Scale Assumptions
NASA’s 2030 US scenarios make the physical scale of a high-impact network explicit. They are internal model assumptions and outputs, not committed production or construction plans.
- NASA’s air-metro model used 2–5 passengers per vehicle, with an average load of 3 per trip; its air-taxi model also used 2–5 passenger vehicles but assumed one passenger per trip in its primary cost model.
- An illustrative air-metro trip used about 10 minutes for 25 miles at a 150 mph average speed, excluding access and handling time.
- NASA assumed 3 new passengers boarded at each stop for the next new air-metro trip and used 14 passenger rides per hour as a high-impact operating variable.
- The modeled network used 2,500–3,500 vertiports, while vehicles were assumed unavailable 15% of the time for fleet-sizing purposes.
| 2030 NASA modeled output | Range or value | Scope |
|---|---|---|
| Air-metro vehicles produced | 35,000–40,000 | US UAM scenario |
| Air-taxi vehicles produced | 20,000–25,000 | US UAM scenario |
| Early-market vehicle useful life | 7–13 years | US scenario estimate |
The useful-life estimate of roughly 7–13 years assumes rapidly changing technology and high utilization. The vehicle ranges are scenario outputs based on assumed demand, throughput and downtime. NASA, “Urban Air Mobility: Concepts and Challenges”
Urban Air Mobility Delivery, Infrastructure and Cost Benchmarks
Some urban-air-mobility activity is adjacent UAS delivery rather than passenger UAM. NASA’s delivery figures are therefore labeled separately from air-metro and air-taxi scenarios.
- NASA’s last-mile delivery scenario projected 0.5 billion deliveries in 2030, used 40,000 vehicles, and showed a modeled price of $4.20 per delivery in 2030 dollars.
- Receiving-vessel capital expenditure was estimated at $10,000–$15,000 per vessel in the 2030 delivery infrastructure model.
- Low-cost delivery charging docks were estimated at $50–$100 each, while an air-metro supercharger was estimated at $100,000–$150,000 per station.
- Central air-traffic-management infrastructure was estimated at $30 million–$50 million in the 2030 US scenario.
- One large-vehicle supply scenario estimated $250,000–$300,000 per vehicle for all components and $30,000–$50,000 for the sensing system.
- NASA used $10,000 per skilled operator as an approximate training cost in its supply assumptions.
These are modeled infrastructure and supply estimates, not observed procurement prices or market tariffs. The NASA cost context is a study oriented toward 2017 dollars, while the delivery price is shown in the model as 2030 dollars. NASA, “Urban Air Mobility: Concepts and Challenges”
UAM Safety, Noise, Emissions and Adoption Constraints
Adoption depends on more than stated enthusiasm: service access, price sensitivity, travel time, noise, safety and environmental performance all shape the market. The evidence here mixes EU perceptions, UK and US scenarios, and related German RAM choice models, so the measures should not be treated as one common adoption series.
NASA’s air-taxi model assumed door-to-door stops within less than 3 minutes’ walking time, while its delivery model targeted package pickup walking time below 5–10 minutes round trip. These stringent service-design assumptions help explain why dense vertiport or receiving infrastructure appears in high-impact scenarios.
The UK report’s modeled UAM take-up remained below 1% in all scenarios, from 0.12% to 0.84% of initial taxi distance. This is an entry-into-service model input or output for a new market, not a measured adoption rate. UK Department for Transport, “Future of Flight Scenarios Futures Research Report”
The German 19-seat hybrid-electric commuter model reported cost elasticities of −0.87 for non-business travelers and −0.82 for business travelers. It also reported time elasticities of −0.42 for the hybrid-electric commuter and −0.44 for the conventional commuter among non-business travelers; these are stated-choice estimates for regional mobility alternatives. Journal of Air Transport Management, “User preferences for Regional Air Mobility: Insights from Germany”
The same German study estimated willingness to pay for rail’s CO2-equivalent reduction at €0.72 per kg for non-business travelers and €1.55 per kg for business travelers. That is a valuation of rail emissions reduction, not a measured UAM emissions outcome, and should not be used as a lifecycle emissions estimate.