Kamal Acharya

Ph.D. Candidate at UMBC, AI Researcher

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Journal Article

Demand Modeling for Advanced Air Mobility: Challenges, Opportunities, and Future Directions

A comprehensive IEEE T-ITS survey on Advanced Air Mobility demand modeling, covering econometric, discrete choice, simulation, machine learning, and hybrid approaches for future air transportation systems.

2026 IEEE Transactions on Intelligent Transportation Systems DOI: 10.1109/TITS.2026.3671002

Advanced Air Mobility Demand Modeling Intelligent Transportation Systems

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Abstract

Advanced Air Mobility (AAM) has attracted increasing research and industry attention as a potential extension of passenger and cargo transportation systems, supported by developments in electric vertical take-off and landing (eVTOL) aircraft, autonomy, and integrated traffic management. Unlike conventional transportation systems, AAM presents unique challenges to demand modeling due to untested routes, new vehicle classes, and evolving user behavior. This paper conducts a comprehensive survey of demand modeling approaches relevant to AAM, encompassing econometric models, discrete choice frameworks, simulation-based approaches, machine learning techniques, and hybrid methods. Our evaluation focuses on four core modeling stages: trip generation, trip distribution, mode choice, and assignment. We further analyze the key factors influencing demand and assess their implications for model design and forecasting accuracy. Research gaps are identified in areas such as airspace routing, vertiport placement, capacity constraints, weather resilience, and community acceptance. To address these, we highlight the need for infrastructure-aware, behaviorally grounded, and real-time adaptive demand models. We outline future research opportunities including fine-grained temporal and spatial resolution, multimodal integration, induced demand representation, and equity considerations. By categorizing current methods and synthesizing emerging directions, this survey establishes a critical research agenda to guide policymakers, practitioners, and researchers in advancing robust and socially responsible AAM demand forecasting.

Plain-Language Summary

This paper explains how researchers and planners can estimate future demand for air taxi, regional air mobility, and related AAM services when the market, routes, infrastructure, and user behavior are still emerging.

Why This Paper Matters

Advanced Air Mobility cannot be planned only around aircraft technology. Its success depends on whether travelers will use the service, where demand will emerge, how it will interact with existing transportation modes, and whether infrastructure such as vertiports, charging systems, and airspace corridors can support that demand. This paper provides a structured foundation for researchers, planners, and policymakers to build more realistic AAM demand forecasting models.

Research Summary

This paper addresses a central planning challenge for Advanced Air Mobility: demand is difficult to model because routes, vehicles, infrastructure, traveler preferences, and service patterns are still evolving. Traditional transportation demand methods are useful, but AAM introduces new constraints around vertiports, airspace, weather, reliability, pricing, and community acceptance.

The study reviews major modeling families used for AAM demand forecasting, including econometric models, discrete choice models, simulation-based approaches, machine learning, and hybrid frameworks. It organizes these methods around the classic transportation modeling pipeline: trip generation, trip distribution, mode choice, and assignment.

A major contribution of the paper is its research agenda. It argues that future AAM demand models should be infrastructure-aware, behaviorally grounded, adaptive to real-time conditions, and attentive to equity and multimodal integration. This makes the paper useful for both academic researchers and transportation planners.

Four-Step AAM Demand Modeling Framework

1

Trip Generation

Identifies potential AAM trips from existing travel patterns, commuting flows, airport-access trips, regional travel demand, cargo movement, and emergency service needs.

2

Trip Distribution

Estimates spatial origin-destination flows and candidate AAM corridors using gravity models, OD matrices, spatial interaction models, and scenario-based assumptions.

3

Mode Choice

Estimates how travelers may choose AAM over cars, public transit, ride-hailing, or conventional air travel based on time, cost, access, reliability, safety perception, and willingness to pay.

4

Assignment

Allocates AAM trips to routes, vertiports, time periods, and operational networks while considering fleet availability, capacity, weather, charging, and airspace constraints.

Key Contributions

  • Surveys major methodological families used for AAM demand modeling.
  • Maps AAM demand research to trip generation, distribution, mode choice, and assignment.
  • Identifies infrastructure, behavioral, operational, and equity gaps in current forecasting practice.
  • Develops a research agenda for robust and socially responsible AAM demand forecasting.

Modeling Approaches Reviewed

Econometric Models

Useful for strategic demand estimation using socioeconomic, geographic, and travel variables.

Discrete Choice Models

Useful for modeling traveler decisions and modal split between AAM and competing modes.

Simulation-Based Models

Useful for testing operational feasibility, vertiport congestion, fleet scheduling, and network performance.

Machine Learning Models

Useful for data-driven forecasting, demand hotspot detection, clustering, and adaptive prediction.

Hybrid Models

Promising for integrating behavior, infrastructure, vehicle performance, and operational constraints into one modeling framework.

Research Gaps

Airspace routing Vertiport placement Capacity constraints Weather resilience Community acceptance Multimodal integration Induced demand Equity

Publication Details

Type
Journal Article
Venue
IEEE Transactions on Intelligent Transportation Systems
Year
2026
Published
March 16, 2026
Pages
1-21

Authors

Research Topics

Advanced Air Mobility Demand Modeling Intelligent Transportation Systems

Citation

@ARTICLE{11435518,
  author={Acharya, Kamal and Raza, Waleed and Vasiloff, Katherine and Wang, Zhenbo and Sun, Liang and Song, Houbing Herbert},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  title={Demand Modeling for Advanced Air Mobility: Challenges, Opportunities, and Future Directions},
  year={2026},
  volume={},
  number={},
  pages={1-21},
  keywords={Active appearance model;Atmospheric modeling;Urban air mobility;Random access memory;Aircraft;Surveys;Biological system modeling;Aircraft propulsion;Adaptation models;Analytical models;Advanced air mobility;air transportation;data analytics and data science;transportation demand modeling and estimation},
  doi={10.1109/TITS.2026.3671002}}