Kamal Acharya

Postdoctoral Research Associate at Baylor University

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Conference Paper

Urban Air Mobility Flight Demand Modeling for Airports in New York City

By Kamal Acharya Houbing Herbert Song Katherine Vasiloff Zhenbo Wang Liang Sun

An AIAA SCITECH 2026 paper on forecasting airport-access Urban Air Mobility demand in New York City by combining 2023 TLC trip records, taxi-zone features, generalized trip cost, and travel-time reliability.

2026 AIAA SCITECH 2026 Forum DOI: 10.2514/6.2026-1475

Urban Air Mobility Airport Demand Modeling Advanced Air Mobility

DOI Publisher Page Cite

Abstract

Rapid urbanization has intensified congestion and travel delays in New York City, motivating interest in Urban Air Mobility (UAM) as a faster and more reliable option for accessing the region’s three major airports: John F. Kennedy (JFK), LaGuardia (LGA), and Newark Liberty (EWR). This paper develops a data-driven framework for forecasting UAM demand by integrating 2023 New York City Taxi & Limousine Commission trip records with socioeconomic and geospatial taxi-zone features. A Generalized Cost of Trip (GCT), combining monetary cost, value of time (VOT), and value of reliability (VOR), is computed for airport-bound origin–destination pairs and used to estimate mode-switching probabilities across early, mid, and mature UAM phases. Results show that several high-congestion Manhattan zones exhibit lower UAM GCT than taxis even in the early phase for all three airports, while Queens and Brooklyn become competitive as UAM costs decline and cruise speeds increase. Hourly switching patterns reveal that reliability-sensitive peak periods drive the highest adoption, with mature-phase conditions capturing over 80–95% of airport trips from major pickup zones. These findings demonstrate that UAM offers increasing advantages for airport access as congestion and technology evolve, providing quantitative guidance for fleet sizing, vertiport siting, pricing strategies, and phased deployment planning in dense metropolitan regions.

In brief

What This Work Does

The paper estimates when airport air taxi trips could beat ground taxi trips in New York City. It shows that UAM is most attractive for congested, reliability-sensitive airport trips, especially from Manhattan and during peak travel periods.

Research impact

Why It Matters

Airport access is one of the most plausible early UAM markets because travelers place high value on time and reliability. This paper gives planners a quantitative way to identify which NYC origin zones, boroughs, hours, and deployment phases are most likely to support viable UAM service.

Paper at a glance

Research Scope

4Research stages
5Methods or application areas
6Future research directions

NYC Airport UAM Demand Modeling Framework

1

Data Processing

Collects 2023 Yellow Taxi, Green Taxi, and HV-FHV trip records, removes missing values, outliers, unknown zones, and non-airport trips, then aggregates records into hourly airport-bound OD flows.

2

Region and Airports

Models demand at NYC taxi-zone resolution for JFK, LaGuardia, and Newark Liberty, represented by TLC taxi zones 132, 138, and 1.

3

Generalized Cost

Calculates taxi GCT from fare, value of time, travel-time variability, and distance variability, while calculating UAM GCT from cost per mile, orthodromic distance, cruise speed, and value of time.

4

Mode Choice

Uses a probabilistic mode-choice formulation to estimate UAM switching probabilities from the difference between taxi and UAM generalized costs.

Key Contributions

  • Develops a data-driven airport-access UAM demand framework for JFK, LaGuardia, and Newark Liberty using 2023 NYC TLC trip records.
  • Integrates socioeconomic and geospatial taxi-zone features with hourly airport-bound origin-destination travel data.
  • Computes generalized cost of trip for taxis and UAM using monetary cost, value of time, and value of reliability.
  • Models mode-switching probability across early, mid, and mature UAM phases using phase-specific cost and cruise-speed assumptions.
  • Identifies spatial and hourly adoption patterns that can inform fleet sizing, vertiport siting, pricing, and phased deployment planning.

Methods and Assumptions

Airport Access Market

Centers the analysis on airport-bound trips to JFK, LGA, and EWR, where congestion and schedule reliability strongly affect traveler preferences.

Reliability-Aware GCT

Extends the generalized cost of trip beyond fare and average time by adding value of reliability for travel-time and distance variability.

UAM Phase Scenarios

Defines early, mid, and mature UAM phases with cost assumptions of $15, $10, and $5 per mile and cruise speeds of 125, 150, and 175 mph.

Hourly Switching

Evaluates mode-switching probabilities by hour to show how peak-period congestion and reliability sensitivity affect adoption.

Borough and Zone Analysis

Compares trip-weighted average generalized cost and switching behavior across top taxi zones and major NYC boroughs.

Research Gaps

  1. Travel time reliability
  2. Temporal uncertainty
  3. Vertiport siting
  4. Pricing strategy
  5. Multimodal integration
  6. Local calibration

Publication Details

Type
Conference Paper
Venue
AIAA SCITECH 2026 Forum
Year
2026
Pages
1-12

Research Topics

Urban Air Mobility Airport Demand Modeling Advanced Air Mobility Generalized Cost of Trip Value of Reliability

Citation

@inproceedings{acharya2026urban,
  author={Acharya, Kamal and Song, Houbing and Vasiloff, Katherine and Wang, Zhenbo and Sun, Liang},
  title={Urban Air Mobility Flight Demand Modeling for Airports in New York City},
  booktitle={AIAA SCITECH 2026 Forum},
  year={2026},
  pages={1--12},
  doi={10.2514/6.2026-1475}
}