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Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities
Suchithra Rajendran, Sharan Srinivas
TL;DR
Urban congestion motivates air taxi service as an emerging on-demand aerial transport system, but its operations and development remain insufficiently established. The paper reviews current ATS research and related literature, identifies operational challenges, and proposes future research opportunities. It highlights limited demand evidence, real-time ride-matching differences, pricing findings, and pilot workforce constraints.
Problem
ATS faces limited operational knowledge, including uncertain demand, ride-matching requirements, pricing issues, maintenance needs, and pilot workforce challenges.
Method
The paper critically reviews air taxi systems, associated operations, current developments, related literature, and future research opportunities from an operations management perspective.
Results
The review organizes current ATS developments around demand prediction, network design, and vehicle configuration, and identifies ride-matching, pricing, maintenance, and pilot training as operational challenges.
Takeaways & Limitations
Future ATS research should adapt relevant methods while accounting for real-time matching, distinct pickup and drop-off locations, uncertain demand, and workforce requirements.
Takeaways & Limitations
ATS demand estimation is constrained by limited eVTOL historical data, potentially high near-term service costs, uncertain public perception, and changing telecommuting patterns.
Abstract
from arXiv · showhide
Expected to operate in the imminent future, air taxi service (ATS) is an aerial on-demand transport for a single passenger or a small group of riders, which seeks to transform the method of everyday commute. This uncharted territory in the emerging transportation world is anticipated to enable consumers bypass traffic congestion in urban road networks. By adopting an electric vertical takeoff and landing concept (eVTOL), air taxis could be operational from skyports retrofitted on building rooftops, thus gaining advantage from an implementation standpoint. Motivated by the potential impact of ATS, this study provides a review of air taxi systems and associated operations. We first discuss the current developments in the ATS (demand prediction, air taxi network design, and vehicle configuration). Next, we anticipate potential future challenges of ATS from an operations management perspective, and review the existing literature that could be leveraged to tackle these problems (ride-matching, pricing strategies, vehicle maintenance scheduling, and pilot training and recruitment). Finally, we detail future research opportunities in the air taxi domain.
1. Introduction
Air taxi service is presented as an emerging urban mobility option intended to address road congestion through on-demand aerial transport. The paper frames ATS operations across strategic, tactical, and operational decisions and reviews its trip structure, current development, challenges, and research opportunities.
- 1.1 Background: Urban congestion motivates ATS, with commuters in metropolitan cities spending over 90 minutes in traffic and congestion producing 330 grams per mile of CO2 emissions.
- 1.1 Background: ATS is an emerging aviation ride-hailing concept that several logistics companies and aviation agencies expect to launch in forthcoming years.
- 1.1 Background: Aircraft manufacturers and technology companies are developing air taxis, including electric aviation taxi projects such as Cora and Airbus vehicles.
- 1.1 Background: ATS operations involve strategic, tactical, and operational decisions, with vertiport location and pilot recruitment representing strategic and medium-term resource choices.
- 1.2 Overview of Urban Air Taxi Service: A typical ATS trip combines on-road travel to a skyport, an air-taxi segment between skyports, and on-road or walking travel to the final destination.
- 1.3 Contribution and Organization: The paper reviews air taxi design and operations, identifies operational challenges, surveys related research, and establishes future opportunities to improve ATS operational efficiency.
2. Current Developments
This section reviews current state-of-the-art developments in air taxi systems. It focuses on demand prediction, network design, and vehicle configuration.
- 2. Current Developments: Current air taxi research is reviewed across demand prediction, network design, and vehicle configuration.
2.1 Demand Prediction for Air Taxis
Air taxi demand prediction must address geographically distributed, time-varying demand despite limited eVTOL history and uncertain customer acceptance. Existing work uses qualitative surveys and focus groups, while future operational forecasting can adapt on-demand mobility methods.
- Demand uncertainty and forecasting challenges: Unanticipated pickup requests can imbalance air-taxi fleets across skyports, increasing customer wait times and reducing fleet utilization, satisfaction, and revenue.
- Demand uncertainty and forecasting challenges: Demand forecasting is constrained by scarce eVTOL historical data, potentially high near-term service costs, uncertain public perceptions, and rising telecommuting.
- Qualitative approaches: Qualitative demand studies use surveys and focus groups, while alternative strategies include borrowing data from other on-demand mobility services.
- Qualitative approaches: Early qualitative research identifies high-income households as likely early adopters and airport transfers, city transfers, and daily commuting as leading market segments.
- Qualitative approaches: Survey-based choice modeling finds travel time and cost most influential, with air-taxi demand highly polarized and a sufficiently large persuadable customer base.
- Quantitative approaches: Once services operate, short-term demand forecasting can adapt time-series and machine-learning methods from on-demand mobility, commonly using GPS traces and sometimes multiple data sources.
2.2 Air Taxi Network Design
Air-taxi network design is a major implementation barrier requiring sites that combine demand density, safe flight operations, charging capacity, and accessibility. Research considers both reuse of existing facilities and analytical site-selection methods.
- Network design: Intracity operational infrastructure is a significant air-taxi implementation barrier, and strategic station locations depend on demand, safety, charging space, and accessibility.
- Network design: Existing structures can accelerate implementation and lower short-term overhead costs.
- Existing facilities: Interstate turnabouts offer large open areas for vertiports and surrounding space for charging, repair, maintenance, and vehicle docking.
- Existing facilities: Turnabouts may also help reduce perceived noise by blending eVTOL sound with surrounding noise.
- Analytical site selection: An iterative constrained-clustering model identified 21 potential skyport sites across New York City boroughs, with especially dense clusters near JFK Airport and South Central Park.
2.3 Air Taxi Configuration
The review identifies three primary eVTOL air-taxi configurations—vectored thrust, lift + cruise, and wingless multicopter—whose performance characteristics and trade-offs differ. Selecting their mix is a strategic decision balancing responsiveness, operating cost, and utilization.
- Three primary eVTOL classifications are vectored thrust, lift + cruise, and wingless multicopter.
- Vectored Thrust: Vectored-thrust aircraft redirect engine thrust through tilt-wing or tilt-rotor designs, enabling higher cruise speeds.Tilt-wing propellers pivot with the wing, whereas tilt-rotors pivot independently of the fixed wing or structure.
- Lift + Cruise: Lift + cruise aircraft use independent vertical-lift and horizontal-cruise engines, but their complexity limits widespread adoption.The design remains viable for air-taxi applications and can support higher cruising speeds.
- Wingless Multicopter: Wingless multicopters use multiple fixed rotors, offering straightforward rotor mechanics, superior flight control, and reduced noise and vibration.Airbus CityAirbus exemplifies a fully electric, autonomous quadrotor with four ducted propellers.
- Each design has strengths and weaknesses across range, speed, passenger capacity, and environmental impact, requiring an optimal category mix.The fleet mix creates a trade-off between service responsiveness, operating cost, and air-taxi utilization.
3. Challenges
The review frames ATS operations as interconnected challenges involving ride-matching, pricing, maintenance, and pilot workforce planning. Existing on-demand mobility and aviation research offers adaptable approaches, but ATS requires models addressing its distinctive real-time, multi-segment, and integrated operations.
- Ride-Matching: Ride-matching must assign requests to vehicles while minimizing customer wait time and vacant trips across ATS’s multi-segment journeys.More trip segments increase potential matches and the computational time needed to enumerate combinations.
- Ride-Matching: Existing matching approaches include first-dispatch, batching, and dynamic matching, with assignments based on factors such as pickup time, empty travel, and vehicle distribution.On-road ATS matching can adapt methods developed for services such as Uber and Lyft.
- Ride-Matching: ATS ride-matching differs from DAFP because it requires real-time matching with very small LBRD and distinct pickup and drop-off locations.These differences must be considered when adapting DAFP models to ATS’s on-air segment.
- Pricing Strategies: Dynamic pricing can benefit drivers and platforms in two-sided markets, while customers benefit only during off-peak hours.Threshold-based pricing was robust to system uncertainties but offered insignificant advantages over static pricing in a single-region market.
- Pricing Strategies: ATS pricing must account for base fare, distance, duration, booking fees, multipliers, promotions, and ridesharing discounts amid likely spatiotemporal imbalance.The review suggests adapting dynamic-pricing research for ATS, which is likely to operate as a one-sided market.
- Fleet Maintenance: Maintenance scheduling must address changing flight schedules, varied aircraft service requirements, high operating costs, strict regulations, and complex coordination.Prior studies use integer programming, heuristics, intelligent engines, and genetic algorithms to develop maintenance schedules.
- Pilot Training and Scheduling: Pilot planning is challenging because pilots remain necessary before eventual autonomy, while training and certification requirements can be time-consuming.The cited Part 135 requirements include 500 PIC hours, 500 VFR hours, 1200 IFR hours, and 50 powered-lift PIC hours.
- Pilot Training and Scheduling: The proposed pilot framework first minimizes the number of pilots needed across slots, then assigns pilots to shifts while maximizing stated preferences.Phase 1 determines slot staffing; Phase 2 uses preference weights and assignments represented by Y_ij.
4. Opportunities for Future Research Directions
The review identifies under-explored operational problems spanning fleet procurement, facility capacity and selection, pricing, maintenance, routing, and multimodal integration. It proposes adapting established optimization and decision-analysis approaches to ATS-specific constraints.
- Fleet and infrastructure: Fleet procurement must balance costly excess inventory against vehicle shortages that increase waits or reject requests, while accounting for differing aircraft configurations.Advance demand estimation is especially important because air taxis have long production times.
- Fleet and infrastructure: Vertiport models should jointly consider facility location, capacity, layout, and the required numbers of maintenance, charging, docking, takeoff, and landing stations.Existing location studies based on demand volume do not fully address facility sizing.
- Fleet and infrastructure: Vertiport selection involves conflicting criteria, including operating cost, public access, population coverage, and site feasibility, motivating MCDA models.Future models could rank candidate locations by weighting or prioritizing decision-maker objectives.
- Pricing: ATS pricing research should adapt predominantly two-sided-market taxi pricing to a likely one-sided market controlled by service providers.Initial strategies could also examine penetration pricing, loyalty rewards, discounted longer rides, day, time, and location effects.
- Maintenance and routing: Maintenance scheduling must coordinate demand estimation with servicing, manpower, and equipment utilization because the logistics company maintains the air-taxi fleet.After each dropoff, the system may choose idling, inspection, maintenance, charging, or another pickup, alongside the next service location.
- Maintenance and routing: Dynamic routing must incorporate vehicle locations, fleet availability, charging and maintenance needs, and customer trip details in real-time decisions.Integrated ground-air scheduling can extend dynamic Dial-a-Ride with Transfers models to large-scale ATS and multiple eVTOL segments.
viii. Leveraging Mobility as a Service (MaaS) Facility for ATS: The incorporation
The review outlines future ATS opportunities in multimodal scheduling, data-enabled demand prediction, logistics, pilot management, and pilotless operations. These directions can draw on MaaS concepts, sensor data, mathematical optimization, and AI methods.
- MaaS and multimodal integration: Joint scheduling with buses, subways, trams, cars, and air taxis could expand passenger travel options at reduced cost.Examples include bus → air taxi → subway and tram → air taxi → car itineraries.
- Data and prediction: Once operational, IoT-enabled air taxis could supply trip, weather, passenger, GPS, and battery data for dispatching and machine-learning demand prediction.Relevant predictors include day, time, location, temperature, and weather conditions.
- Supply-chain logistics: Hybrid air-taxi–UAV systems could dispatch multiple drones from one air taxi for parallel low-weight last-mile deliveries.The concept may be particularly useful when disasters damage roads and bridges.
- Pilot operations: Pilot planning should determine staffing levels and assignments while incorporating long training times, manpower costs, and evolving FAA requirements.Pilot-to-aircraft assignment models could parallel customer ride-matching algorithms.
- Pilot operations: Pilotless VTOL research could use onboard computers interfaced with ground control centers to address manpower expense and pilot-related load-capacity reductions.The review frames integration of pilotless designs with other proposed research directions as a future opportunity.
- Integrated decision-making: Demand forecasts inform fleet requirements, while fleet size and demand inform vertiport location, capacity, and layout decisions.Mathematical programming and AI or machine learning are identified as relevant tools for optimizing and scaling these operational decisions.
5. Concluding Remarks
The paper reviews ATS systems, operations, challenges, and future research opportunities from an operations-management perspective. It synthesizes current work and identifies directions intended to improve understanding and operational efficiency.
- Scope and contribution: The review covers ATS demand prediction, network design, vehicle configurations, ride-matching, pricing, maintenance scheduling, and pilot training and recruitment.It also presents future research topics across these core areas.
- Scope and contribution: The authors position the article as a survey of air-taxi systems predominantly from an operations-management perspective.The paper aims to support academics and practitioners examining ATS operations and research challenges.