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

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.